repo_name stringlengths 8 38 | pr_number int64 3 47.1k | pr_title stringlengths 8 175 | pr_description stringlengths 2 19.8k ⌀ | author null | date_created stringlengths 25 25 | date_merged stringlengths 25 25 | filepath stringlengths 6 136 | before_content stringlengths 54 884k ⌀ | after_content stringlengths 56 884k | pr_author stringlengths 3 21 | previous_commit stringlengths 40 40 | pr_commit stringlengths 40 40 | comment stringlengths 2 25.4k | comment_author stringlengths 3 29 | __index_level_0__ int64 0 5.1k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | docs/source/example_notebooks/sensitivity_analysis_nonparametric_estimators.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "0bbaacaa",
"metadata": {},
"source": [
"# Sensitivity analysis for non-parametric causal estimators\n",
"Sensitivity analysis helps us study how robust an estimated effect is when the assumption of no unobserved confounding is violated. That is, how ... | {
"cells": [
{
"cell_type": "markdown",
"id": "0bbaacaa",
"metadata": {},
"source": [
"# Sensitivity analysis for non-parametric causal estimators\n",
"Sensitivity analysis helps us study how robust an estimated effect is when the assumption of no unobserved confounding is violated. That is, how ... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | it will change with every commit we modify the notebook (if we are using vscode) | andresmor-ms | 184 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | some methods do support multiple treatments. especially the EconML ones.
That's why we always pass the treatment list, as you can see. whereas for outcome, we pass the first element of the list | amit-sharma | 185 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | docstring `treatment` should be `treatment_name`. | amit-sharma | 186 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | Changing the value of `need_conditional_estimates` is a side-effect of this method. Will be good to mention in the docstring. | amit-sharma | 187 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | In the old code, the `estimate` param was used to initialize the different variables. But now we are using the `self` object. The estimate param becomes redundant. We can remove it | amit-sharma | 188 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | why is the typehint in quotes? | amit-sharma | 189 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | should we define the signature of fit and effect methods here so that the signature is enforced for child estimators? We can simply return "raise NotImplementedError" --conveying that child classes have to implement this method. | amit-sharma | 190 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | docstring needs to be updated.
* estimator is not referenced. | amit-sharma | 191 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | why is identified_estimand removed from the params? Logically, a user should provide the output of identify_effect to this function.
I see that you are using the estimator's target_estimand, but then having a str parameter feels odd. What are the possible values for identifier name?
Rather than identifier_name, I ... | amit-sharma | 192 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | test_significance is not used anywhere in this method. It should be passed to the estimator. | amit-sharma | 193 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | can rename it to `get_new_estimator_object` to make it clear that it is not returning the same old estimator, since it is an instance method now | amit-sharma | 194 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | fit_estimator is not needed. We can remove the `fit` code from this method and expect refuters to call it explicitly. | amit-sharma | 195 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | in the docstring below, need to update how estimator is provided. Providing strings is not allowed. | amit-sharma | 196 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | Oh, I copied this comment from: https://github.com/py-why/dowhy/blob/main/dowhy/causal_estimator.py#L109 I guess that it is a legacy comment that was never removed?
If that's the case I'll need to update the types for all treatment parameters and the docs, because from the text of it (at least to me) it says that i... | andresmor-ms | 197 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | This is a forward reference, since CausalEstimate is defined below the CausalEstimator class we need to do use the quotes, otherwise we get a syntax error https://peps.python.org/pep-0484/#forward-references | andresmor-ms | 198 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | The issue with creating abstract methods here is that we would essentially set the method signature (including parameters) so we would make all the effect and fit methods take the same parameters for all estimators (preventing new estimators to take different parameters or having a bunch of unneeded parameters in all e... | andresmor-ms | 199 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | What information from the removed parameter are we missing from the estimator target_estimand? shouldn't they be the same? the identifier name replaces the first part of the previous parameter `method_name` which was a string. According to the docs it:
> Currently requires an explicit method name to be specified. Me... | andresmor-ms | 200 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | got it, I understand the motivation now. | amit-sharma | 201 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | oh I see. yeah, that's my bad. I may have updated the code without the docstring.
But yes, we do support multiple treatments for some methods, that's why treatment is always passed as a list. | amit-sharma | 202 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | Good point. There's another case to consider, where a user will not provide any estimator and the function will automatically find the right Estimator and initialize it. This is the direction we'll be moving in the future. In that case, we would need the `identified_estimand` parameter.
Also, the identifier_name par... | amit-sharma | 203 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | Can we avoid this method? At least, I think we should not make it part of the contract of a `CausalEstimator`. If some implementation of `CausalEstimator` needs the data after `fit`, that specific implementation is still free to store it.
I think having such a method is too inviting to get into the business of very ... | petergtz | 204 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | This is similar to `set_data` above. Since it seems like this is common functionality needed in multiple places, how about defining a module-based (not class-based):
```python
def `effect_modifiers(effect_modifier_names: ...) -> Tuple[... , ... ,...]:
...
```
and then calling it in those places where it is n... | petergtz | 205 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | Good point. I think we can avoid storing these data-based objects completely.
For context, the earlier version had this code inside the init method of the estimator. But now the init method does not get access to the data, only fit method gets access to the data (as in sklearn API). That's why this method needed to... | amit-sharma | 206 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | I'll take a look at this, I tried to do this in a previous version but found out that self._data was used in several places and decided to go with setting self._data and focus on getting the fit() to work correctly, if this becomes a big change, would you agree that it could go into another PR? @amit-sharma @petergtz | andresmor-ms | 207 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | That's a good idea, tbh I didn't like the `set_*` methods I created, but as I mentioned in a comment before I was focusing on getting the fit() to work correctly :) | andresmor-ms | 208 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | Agree with @amit-sharma that ideally we wouldn't have to store this data at all. But to avoid delaying this PR too much, I'd be fine by simply renaming this method to `_set_data` to make it very explicit, that this is not part of the API of that class. | petergtz | 209 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | yeah, let's just make the `set` methods as private for this PR, as Peter suggested. This will be work for a future PR | amit-sharma | 210 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | `test_significance` is used only on the estimate.add_params, this is because at this point the Estimator is already instantiated by the CausalModel and the `test_significance` was already passed by the `CausalModel.estimate_effect` method, we don't need them as parameter here | andresmor-ms | 211 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | okay, let's move this discussion for the next PR. | amit-sharma | 212 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/distance_matching_estimator.py | import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
class DistanceMatchingEstimator(CausalEstimator):
"""Simple matching estimator for binary treatments based on a distance
metric.
For a list of standard... | from typing import Any, List, Optional, Union
import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_identifier import IdentifiedEstimand
class DistanceMatchingEstimator(CausalEstimator):
"""Simpl... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | need a docstring for this method. | amit-sharma | 213 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/distance_matching_estimator.py | import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
class DistanceMatchingEstimator(CausalEstimator):
"""Simple matching estimator for binary treatments based on a distance
metric.
For a list of standard... | from typing import Any, List, Optional, Union
import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_identifier import IdentifiedEstimand
class DistanceMatchingEstimator(CausalEstimator):
"""Simpl... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | Also, how do you feel about renaming this instance method to simply "effect"? Its shorter and the meaning is clear because we do `Estimator.effect()`. If you agree we can change it for all estimators. | amit-sharma | 214 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/distance_matching_estimator.py | import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
class DistanceMatchingEstimator(CausalEstimator):
"""Simple matching estimator for binary treatments based on a distance
metric.
For a list of standard... | from typing import Any, List, Optional, Union
import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_identifier import IdentifiedEstimand
class DistanceMatchingEstimator(CausalEstimator):
"""Simpl... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | I like it, I just didn't know if we actually wanted to rename it. I'll rename it in the estimators but leave the estimate_effect method name in the CausalModel otherwise we might break backwards compatibility. | andresmor-ms | 215 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/distance_matching_estimator.py | import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
class DistanceMatchingEstimator(CausalEstimator):
"""Simple matching estimator for binary treatments based on a distance
metric.
For a list of standard... | from typing import Any, List, Optional, Union
import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_identifier import IdentifiedEstimand
class DistanceMatchingEstimator(CausalEstimator):
"""Simpl... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | That sounds good! | amit-sharma | 216 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/distance_matching_estimator.py | import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
class DistanceMatchingEstimator(CausalEstimator):
"""Simple matching estimator for binary treatments based on a distance
metric.
For a list of standard... | from typing import Any, List, Optional, Union
import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_identifier import IdentifiedEstimand
class DistanceMatchingEstimator(CausalEstimator):
"""Simpl... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | Will rename in future PR as there are other places where the `effect()` function exist. | andresmor-ms | 217 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/econml.py | import inspect
from importlib import import_module
from typing import Callable
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wra... | import inspect
from importlib import import_module
from typing import Any, Callable, List, Optional, Protocol, Union
from warnings import warn
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_i... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | needs a docstring | amit-sharma | 218 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/econml.py | import inspect
from importlib import import_module
from typing import Callable
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wra... | import inspect
from importlib import import_module
from typing import Any, Callable, List, Optional, Protocol, Union
from warnings import warn
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_i... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | Could this already be a `Union[str,EconMLEstimator]` where `EconMLEstimator` is something along the lines of:
```python
class EconMLEstimator(Protocol):
def estimate(self, ...):
...
...
```
Then, when actually using this, you could check if it's a string or not. Long-term, we could deprecate ... | petergtz | 219 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/econml.py | import inspect
from importlib import import_module
from typing import Callable
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wra... | import inspect
from importlib import import_module
from typing import Any, Callable, List, Optional, Protocol, Union
from warnings import warn
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_i... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | +1 This is a good idea to maintain backwards compatibility while still following the new API. | amit-sharma | 220 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/econml.py | import inspect
from importlib import import_module
from typing import Callable
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wra... | import inspect
from importlib import import_module
from typing import Any, Callable, List, Optional, Protocol, Union
from warnings import warn
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_i... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | Yep, I actually created an example of this to show Amit some days ago :) I think we could even deprecate the string now and move the code that creates an econml instance from the string to the CausalModel estimate_effect class, what do you think? @petergtz
And this also applies to the CausalML estimator | andresmor-ms | 221 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/econml.py | import inspect
from importlib import import_module
from typing import Callable
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wra... | import inspect
from importlib import import_module
from typing import Any, Callable, List, Optional, Protocol, Union
from warnings import warn
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_i... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | > we could even deprecate the string now and move the code that creates an econml instance from the string to the CausalModel estimate_effect class
From a point of getting more concrete on this, I like it. But in terms of backwards compatibility, I'm not sure sure we should already be so bold. But that's mostly depe... | petergtz | 222 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/econml.py | import inspect
from importlib import import_module
from typing import Callable
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wra... | import inspect
from importlib import import_module
from typing import Any, Callable, List, Optional, Protocol, Union
from warnings import warn
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_i... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | this is nice way of using the protocol! | amit-sharma | 223 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/econml.py | import inspect
from importlib import import_module
from typing import Callable
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wra... | import inspect
from importlib import import_module
from typing import Any, Callable, List, Optional, Protocol, Union
from warnings import warn
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_i... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | data: dataframe containing the data on which treatment effect is to be estimated.
treatment_value: value of the treatment variable for which the effect is to be estimated.
control_value: value of the treatment variable that denotes its absence (usually 0)
target_units: The units for which the treatment effect should... | amit-sharma | 224 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/generalized_linear_model_estimator.py | import itertools
import statsmodels.api as sm
from dowhy.causal_estimators.regression_estimator import RegressionEstimator
class GeneralizedLinearModelEstimator(RegressionEstimator):
"""Compute effect of treatment using a generalized linear model such as logistic regression.
Implementation uses statsmodels... | import itertools
from typing import Any, List, Optional, Union
import pandas as pd
import statsmodels.api as sm
from dowhy.causal_estimator import CausalEstimator
from dowhy.causal_estimators.regression_estimator import RegressionEstimator
from dowhy.causal_identifier import IdentifiedEstimand
class GeneralizedLine... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | since this is a fit method, `outcome_is_binary` looks out of place. Shall we move it to the `predict_fn` where it is used?
Ideally we would want to avoid side-effects of the fit method. | amit-sharma | 225 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/instrumental_variable_estimator.py | import numpy as np
import sympy as sp
import sympy.stats as spstats
from statsmodels.sandbox.regression.gmm import IV2SLS
from dowhy.causal_estimator import CausalEstimate, CausalEstimator, RealizedEstimand
from dowhy.utils.api import parse_state
class InstrumentalVariableEstimator(CausalEstimator):
"""Compute e... | from typing import Any, Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
import sympy.stats as spstats
from statsmodels.sandbox.regression.gmm import IV2SLS
from dowhy.causal_estimator import CausalEstimate, CausalEstimator, RealizedEstimand
from dowhy.causal_identifier import Ide... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | empty line can be removed.
| amit-sharma | 226 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/instrumental_variable_estimator.py | import numpy as np
import sympy as sp
import sympy.stats as spstats
from statsmodels.sandbox.regression.gmm import IV2SLS
from dowhy.causal_estimator import CausalEstimate, CausalEstimator, RealizedEstimand
from dowhy.utils.api import parse_state
class InstrumentalVariableEstimator(CausalEstimator):
"""Compute e... | from typing import Any, Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
import sympy.stats as spstats
from statsmodels.sandbox.regression.gmm import IV2SLS
from dowhy.causal_estimator import CausalEstimate, CausalEstimator, RealizedEstimand
from dowhy.causal_identifier import Ide... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | needs docstring | amit-sharma | 227 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/instrumental_variable_estimator.py | import numpy as np
import sympy as sp
import sympy.stats as spstats
from statsmodels.sandbox.regression.gmm import IV2SLS
from dowhy.causal_estimator import CausalEstimate, CausalEstimator, RealizedEstimand
from dowhy.utils.api import parse_state
class InstrumentalVariableEstimator(CausalEstimator):
"""Compute e... | from typing import Any, Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
import sympy.stats as spstats
from statsmodels.sandbox.regression.gmm import IV2SLS
from dowhy.causal_estimator import CausalEstimate, CausalEstimator, RealizedEstimand
from dowhy.causal_identifier import Ide... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | effect modifiers are not used by IV method. Can update the docstring and say that effect_modifiers are not supported.
Shall we also raise a valueerror here if a user provides them? | amit-sharma | 228 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/instrumental_variable_estimator.py | import numpy as np
import sympy as sp
import sympy.stats as spstats
from statsmodels.sandbox.regression.gmm import IV2SLS
from dowhy.causal_estimator import CausalEstimate, CausalEstimator, RealizedEstimand
from dowhy.utils.api import parse_state
class InstrumentalVariableEstimator(CausalEstimator):
"""Compute e... | from typing import Any, Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
import sympy.stats as spstats
from statsmodels.sandbox.regression.gmm import IV2SLS
from dowhy.causal_estimator import CausalEstimate, CausalEstimator, RealizedEstimand
from dowhy.causal_identifier import Ide... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | I think it is a better idea to just remove it since it is not used. | andresmor-ms | 229 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/instrumental_variable_estimator.py | import numpy as np
import sympy as sp
import sympy.stats as spstats
from statsmodels.sandbox.regression.gmm import IV2SLS
from dowhy.causal_estimator import CausalEstimate, CausalEstimator, RealizedEstimand
from dowhy.utils.api import parse_state
class InstrumentalVariableEstimator(CausalEstimator):
"""Compute e... | from typing import Any, Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
import sympy.stats as spstats
from statsmodels.sandbox.regression.gmm import IV2SLS
from dowhy.causal_estimator import CausalEstimate, CausalEstimator, RealizedEstimand
from dowhy.causal_identifier import Ide... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | If I remove this the tests will fail, I'll remove it as part of another PR once this one is completed. | andresmor-ms | 230 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/propensity_score_estimator.py | import numpy as np
import pandas as pd
from sklearn import linear_model
from sklearn.exceptions import NotFittedError
from dowhy.causal_estimator import CausalEstimator
class PropensityScoreEstimator(CausalEstimator):
"""
Base class for estimators that estimate effects based on propensity of
treatment as... | from typing import Any, List, Optional, Union
import numpy as np
import pandas as pd
from sklearn import linear_model
from sklearn.exceptions import NotFittedError
from dowhy.causal_estimator import CausalEstimator
from dowhy.causal_identifier import IdentifiedEstimand
class PropensityScoreEstimator(CausalEstimator... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | we do not need this parameter now, recalculate_propensity_score, because we have separate fit and estimate. can remove this | amit-sharma | 231 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/propensity_score_estimator.py | import numpy as np
import pandas as pd
from sklearn import linear_model
from sklearn.exceptions import NotFittedError
from dowhy.causal_estimator import CausalEstimator
class PropensityScoreEstimator(CausalEstimator):
"""
Base class for estimators that estimate effects based on propensity of
treatment as... | from typing import Any, List, Optional, Union
import numpy as np
import pandas as pd
from sklearn import linear_model
from sklearn.exceptions import NotFittedError
from dowhy.causal_estimator import CausalEstimator
from dowhy.causal_identifier import IdentifiedEstimand
class PropensityScoreEstimator(CausalEstimator... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | This method needs to be wrapped inside/moved to the fit method.
It was useful earlier to prevent re-fitting because we did not have an explicit fit method.
We need to support three usecases in the fit method:
1. user provides their own scores in the propensity_score_column of dataframe. In that case, fit is a no... | amit-sharma | 232 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/propensity_score_matching_estimator.py | import pandas as pd
from sklearn import linear_model
from sklearn.neighbors import NearestNeighbors
from dowhy.causal_estimator import CausalEstimate
from dowhy.causal_estimators.propensity_score_estimator import PropensityScoreEstimator
class PropensityScoreMatchingEstimator(PropensityScoreEstimator):
"""Estima... | from typing import Any, List, Optional, Union
import pandas as pd
from sklearn.neighbors import NearestNeighbors
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.propensity_score_estimator import PropensityScoreEstimator
from dowhy.causal_identifier import IdentifiedEsti... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | The line below, "For a list of standards args and kwargs" , can be removed. | amit-sharma | 233 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/propensity_score_matching_estimator.py | import pandas as pd
from sklearn import linear_model
from sklearn.neighbors import NearestNeighbors
from dowhy.causal_estimator import CausalEstimate
from dowhy.causal_estimators.propensity_score_estimator import PropensityScoreEstimator
class PropensityScoreMatchingEstimator(PropensityScoreEstimator):
"""Estima... | from typing import Any, List, Optional, Union
import pandas as pd
from sklearn.neighbors import NearestNeighbors
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.propensity_score_estimator import PropensityScoreEstimator
from dowhy.causal_identifier import IdentifiedEsti... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | do not need recalculate_ps param | amit-sharma | 234 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/propensity_score_matching_estimator.py | import pandas as pd
from sklearn import linear_model
from sklearn.neighbors import NearestNeighbors
from dowhy.causal_estimator import CausalEstimate
from dowhy.causal_estimators.propensity_score_estimator import PropensityScoreEstimator
class PropensityScoreMatchingEstimator(PropensityScoreEstimator):
"""Estima... | from typing import Any, List, Optional, Union
import pandas as pd
from sklearn.neighbors import NearestNeighbors
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.propensity_score_estimator import PropensityScoreEstimator
from dowhy.causal_identifier import IdentifiedEsti... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | the code of "refresh propensity score" should be inside the fit function.
This logic was created because we did not have a fit method. So refresh method checks whether model is fitted already, if not it fits it. We can remove this refresh function and move its code to the fit method. | amit-sharma | 235 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/propensity_score_stratification_estimator.py | import pandas as pd
from sklearn import linear_model
from dowhy.causal_estimator import CausalEstimate
from dowhy.causal_estimators.propensity_score_estimator import PropensityScoreEstimator
class PropensityScoreStratificationEstimator(PropensityScoreEstimator):
"""Estimate effect of treatment by stratifying the... | from typing import Any, List, Optional, Union
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.propensity_score_estimator import PropensityScoreEstimator
from dowhy.causal_identifier import IdentifiedEstimand
class PropensityScoreStratificationEstim... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | do not need refresh propensity score here, just the assignment of model's predictions to the propensity score column | amit-sharma | 236 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/propensity_score_weighting_estimator.py | import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate
from dowhy.causal_estimators.propensity_score_estimator import PropensityScoreEstimator
class PropensityScoreWeightingEstimator(PropensityScoreEstimator):
"""Estimate effect of treatment by weighing the data by
inverse p... | from typing import Any, List, Optional, Union
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.propensity_score_estimator import PropensityScoreEstimator
from dowhy.causal_identifier import IdentifiedEstimand
class PropensityScore... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | recalculate ps param is not needed. | amit-sharma | 237 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/propensity_score_weighting_estimator.py | import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate
from dowhy.causal_estimators.propensity_score_estimator import PropensityScoreEstimator
class PropensityScoreWeightingEstimator(PropensityScoreEstimator):
"""Estimate effect of treatment by weighing the data by
inverse p... | from typing import Any, List, Optional, Union
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.propensity_score_estimator import PropensityScoreEstimator
from dowhy.causal_identifier import IdentifiedEstimand
class PropensityScore... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | same comment for refresh ps method | amit-sharma | 238 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/regression_discontinuity_estimator.py | import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimator
from dowhy.causal_estimators.instrumental_variable_estimator import InstrumentalVariableEstimator
class RegressionDiscontinuityEstimator(CausalEstimator):
"""Compute effect of treatment using the regression discontinuity me... | from typing import Any, List, Optional, Union
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimator
from dowhy.causal_estimators.instrumental_variable_estimator import InstrumentalVariableEstimator
from dowhy.causal_identifier import IdentifiedEstimand
class RegressionDiscontinui... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | up until this line, it may be better to move the code to the fit method. | amit-sharma | 239 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/regression_discontinuity_estimator.py | import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimator
from dowhy.causal_estimators.instrumental_variable_estimator import InstrumentalVariableEstimator
class RegressionDiscontinuityEstimator(CausalEstimator):
"""Compute effect of treatment using the regression discontinuity me... | from typing import Any, List, Optional, Union
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimator
from dowhy.causal_estimators.instrumental_variable_estimator import InstrumentalVariableEstimator
from dowhy.causal_identifier import IdentifiedEstimand
class RegressionDiscontinui... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | treatment_value, control_value etc. should be passed to the IV estimate_effect. | amit-sharma | 240 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/regression_estimator.py | import numpy as np
import pandas as pd
import statsmodels.api as sm
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
class RegressionEstimator(CausalEstimator):
"""Compute effect of treatment using some regression function.
Fits a regression model for estimating the outcome using treatment... | from typing import Any, List, Optional, Union
import numpy as np
import pandas as pd
import statsmodels.api as sm
from dowhy.causal_estimator import CausalEstimate, CausalEstimator, IdentifiedEstimand
class RegressionEstimator(CausalEstimator):
"""Compute effect of treatment using some regression function.
... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | up until line 126, a model is being fit. All this code should be inside fit method. | amit-sharma | 241 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/regression_estimator.py | import numpy as np
import pandas as pd
import statsmodels.api as sm
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
class RegressionEstimator(CausalEstimator):
"""Compute effect of treatment using some regression function.
Fits a regression model for estimating the outcome using treatment... | from typing import Any, List, Optional, Union
import numpy as np
import pandas as pd
import statsmodels.api as sm
from dowhy.causal_estimator import CausalEstimate, CausalEstimator, IdentifiedEstimand
class RegressionEstimator(CausalEstimator):
"""Compute effect of treatment using some regression function.
... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | need docstring | amit-sharma | 242 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/regression_estimator.py | import numpy as np
import pandas as pd
import statsmodels.api as sm
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
class RegressionEstimator(CausalEstimator):
"""Compute effect of treatment using some regression function.
Fits a regression model for estimating the outcome using treatment... | from typing import Any, List, Optional, Union
import numpy as np
import pandas as pd
import statsmodels.api as sm
from dowhy.causal_estimator import CausalEstimate, CausalEstimator, IdentifiedEstimand
class RegressionEstimator(CausalEstimator):
"""Compute effect of treatment using some regression function.
... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | it's the same as for econml estimator. Additionally,
need_conditional_estimates: Boolean flag on whether treatment effect estimates conditional on the effect modifiers are needed. Otherwise, the average treatment effect is returned. | amit-sharma | 243 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/two_stage_regression_estimator.py | import copy
import itertools
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.linear_regression_estimator import LinearRegressionEstimator
from dowhy.causal_identifier.identify_effect import EstimandType
from dowhy.utils.api import ... | import copy
from typing import Any, List, Optional, Type, Union
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.linear_regression_estimator import LinearRegressionEstimator
from dowhy.causal_identifier import EstimandType, Identifi... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | okay this is a major refactor. I did try to check the details, but I just wanted to confirm the logic: each of these models just change the treatment or outcome column and call the user-provided estimators. Will be good to double-check that the name changes have been done correctly. It looks good to me. | amit-sharma | 244 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/two_stage_regression_estimator.py | import copy
import itertools
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.linear_regression_estimator import LinearRegressionEstimator
from dowhy.causal_identifier.identify_effect import EstimandType
from dowhy.utils.api import ... | import copy
from typing import Any, List, Optional, Type, Union
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.linear_regression_estimator import LinearRegressionEstimator
from dowhy.causal_identifier import EstimandType, Identifi... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | why not change the identifier_method to "backdoor" here? For others, we need the data, but do not need it for this one. | amit-sharma | 245 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/two_stage_regression_estimator.py | import copy
import itertools
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.linear_regression_estimator import LinearRegressionEstimator
from dowhy.causal_identifier.identify_effect import EstimandType
from dowhy.utils.api import ... | import copy
from typing import Any, List, Optional, Type, Union
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.linear_regression_estimator import LinearRegressionEstimator
from dowhy.causal_identifier import EstimandType, Identifi... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | both these lines can be moved to the init method. Will make code easier to understand too | amit-sharma | 246 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/two_stage_regression_estimator.py | import copy
import itertools
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.linear_regression_estimator import LinearRegressionEstimator
from dowhy.causal_identifier.identify_effect import EstimandType
from dowhy.utils.api import ... | import copy
from typing import Any, List, Optional, Type, Union
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.linear_regression_estimator import LinearRegressionEstimator
from dowhy.causal_identifier import EstimandType, Identifi... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | these lines can be moved to the init method since they are part of initializing the model correctly. | amit-sharma | 247 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_estimators/two_stage_regression_estimator.py | import copy
import itertools
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.linear_regression_estimator import LinearRegressionEstimator
from dowhy.causal_identifier.identify_effect import EstimandType
from dowhy.utils.api import ... | import copy
from typing import Any, List, Optional, Type, Union
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.linear_regression_estimator import LinearRegressionEstimator
from dowhy.causal_identifier import EstimandType, Identifi... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | these lines can also be moved to init.
Essentially, init creates the constructor for all these estimators. And then fit just fits them. | amit-sharma | 248 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_model.py | """ Module containing the main model class for the dowhy package.
"""
import logging
from itertools import combinations
from sympy import init_printing
import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.graph_learners as graph_learners
import dowhy.utils.... | """ Module containing the main model class for the dowhy package.
"""
import logging
from itertools import combinations
from sympy import init_printing
import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.graph_learners as graph_learners
import dowhy.utils.... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | If I understand correctly, we do not pass the actual params here because we expect the `estimate_effect` call to take care of it, right? | amit-sharma | 249 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_model.py | """ Module containing the main model class for the dowhy package.
"""
import logging
from itertools import combinations
from sympy import init_printing
import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.graph_learners as graph_learners
import dowhy.utils.... | """ Module containing the main model class for the dowhy package.
"""
import logging
from itertools import combinations
from sympy import init_printing
import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.graph_learners as graph_learners
import dowhy.utils.... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | extra_args should also be passed to estimate_effect? Right now, they are ignored. | amit-sharma | 250 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_model.py | """ Module containing the main model class for the dowhy package.
"""
import logging
from itertools import combinations
from sympy import init_printing
import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.graph_learners as graph_learners
import dowhy.utils.... | """ Module containing the main model class for the dowhy package.
"""
import logging
from itertools import combinations
from sympy import init_printing
import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.graph_learners as graph_learners
import dowhy.utils.... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | Yep, here we are initializing the estimator, just creating it, then we need to call `fit()` and then `estimate_effect()` | andresmor-ms | 251 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_model.py | """ Module containing the main model class for the dowhy package.
"""
import logging
from itertools import combinations
from sympy import init_printing
import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.graph_learners as graph_learners
import dowhy.utils.... | """ Module containing the main model class for the dowhy package.
"""
import logging
from itertools import combinations
from sympy import init_printing
import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.graph_learners as graph_learners
import dowhy.utils.... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | `extra_args` is only used for `init_params` which is used to instantiate estimators, method_params are the extra parameters for executing the estimate_effect method | andresmor-ms | 252 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_refuters/add_unobserved_common_cause.py | import copy
import logging
import math
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import scipy.stats
import statsmodels.api as sm
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from tqdm.auto import tqdm
from dowhy.causal... | import copy
import logging
import math
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import scipy.stats
import statsmodels.api as sm
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from tqdm.auto import tqdm
from dowhy.causal... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | add fit call for all estimators. | amit-sharma | 253 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_refuters/bootstrap_refuter.py | import logging
import random
from typing import List, Optional, Union
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from sklearn.utils import resample
from tqdm.auto import tqdm
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_identifier.identified_est... | import logging
import random
from typing import List, Optional, Union
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from sklearn.utils import resample
from tqdm.auto import tqdm
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.econml import ... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | I think we should have an explicit fit method here, to be consistent with the new API. | amit-sharma | 254 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_refuters/bootstrap_refuter.py | import logging
import random
from typing import List, Optional, Union
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from sklearn.utils import resample
from tqdm.auto import tqdm
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_identifier.identified_est... | import logging
import random
from typing import List, Optional, Union
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from sklearn.utils import resample
from tqdm.auto import tqdm
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.econml import ... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | and then make sure that the right effect modifiers and other parameters are passed to the fit method. | amit-sharma | 255 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_refuters/data_subset_refuter.py | import logging
from typing import Optional, Union
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from tqdm.auto import tqdm
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_identifier import IdentifiedEstimand
from dowhy.causal_refuter import CausalRefu... | import logging
from typing import Optional, Union
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from tqdm.auto import tqdm
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.econml import Econml
from dowhy.causal_identifier import IdentifiedEs... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | add fit method. Same comment for all refuters. | amit-sharma | 256 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_refuters/placebo_treatment_refuter.py | import copy
import logging
from enum import Enum
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from tqdm.auto import tqdm
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_identifier.identified_estimand imp... | import copy
import logging
from enum import Enum
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from tqdm.auto import tqdm
from dowhy.causal_estimator import CausalEstimate
from dowhy.causal_estimators.econml import Econml
from dowhy.causal_e... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | do we need this InstrumentalVariableEstimator import? | amit-sharma | 257 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_refuters/placebo_treatment_refuter.py | import copy
import logging
from enum import Enum
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from tqdm.auto import tqdm
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_identifier.identified_estimand imp... | import copy
import logging
from enum import Enum
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from tqdm.auto import tqdm
from dowhy.causal_estimator import CausalEstimate
from dowhy.causal_estimators.econml import Econml
from dowhy.causal_e... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | why is this IV code removed? is it redundant? | amit-sharma | 258 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_refuters/placebo_treatment_refuter.py | import copy
import logging
from enum import Enum
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from tqdm.auto import tqdm
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_identifier.identified_estimand imp... | import copy
import logging
from enum import Enum
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from tqdm.auto import tqdm
from dowhy.causal_estimator import CausalEstimate
from dowhy.causal_estimators.econml import Econml
from dowhy.causal_e... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | it is used now by updating a piece of code i removed by mistake | andresmor-ms | 259 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | dowhy/causal_refuters/placebo_treatment_refuter.py | import copy
import logging
from enum import Enum
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from tqdm.auto import tqdm
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_identifier.identified_estimand imp... | import copy
import logging
from enum import Enum
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from tqdm.auto import tqdm
from dowhy.causal_estimator import CausalEstimate
from dowhy.causal_estimators.econml import Econml
from dowhy.causal_e... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | good catch, I removed it by mistake. | andresmor-ms | 260 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | poetry.lock | [[package]]
name = "absl-py"
version = "1.3.0"
description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
category = "dev"
optional = false
python-versions = ">=3.6"
[[package]]
name = "alabaster"
version = "0.7.12"
description = "A configurable sidebar-enabled Sphinx theme"
category = "m... | [[package]]
name = "absl-py"
version = "1.3.0"
description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
category = "dev"
optional = false
python-versions = ">=3.6"
[[package]]
name = "alabaster"
version = "0.7.12"
description = "A configurable sidebar-enabled Sphinx theme"
category = "m... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | I'm assuming that this file is auto-generated and doesn't need my review, right? | amit-sharma | 261 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | poetry.lock | [[package]]
name = "absl-py"
version = "1.3.0"
description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
category = "dev"
optional = false
python-versions = ">=3.6"
[[package]]
name = "alabaster"
version = "0.7.12"
description = "A configurable sidebar-enabled Sphinx theme"
category = "m... | [[package]]
name = "absl-py"
version = "1.3.0"
description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
category = "dev"
optional = false
python-versions = ">=3.6"
[[package]]
name = "alabaster"
version = "0.7.12"
description = "A configurable sidebar-enabled Sphinx theme"
category = "m... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | yep it is autogenerated | andresmor-ms | 262 |
py-why/dowhy | 737 | Add polynom regressor and classifier to gcm | This replaces the ProductRegressor.
Signed-off-by: Patrick Bloebaum <bloebp@amazon.com> | null | 2022-11-01 15:56:18+00:00 | 2022-11-04 17:32:01+00:00 | dowhy/gcm/auto.py | import warnings
from enum import Enum, auto
from functools import partial
from typing import Callable, List, Optional, Union
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from sklearn import metrics
from sklearn.exceptions import ConvergenceWarning
from sklearn.linear_model import LinearR... | import warnings
from enum import Enum, auto
from functools import partial
from typing import Callable, List, Optional, Union
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from sklearn import metrics
from sklearn.exceptions import ConvergenceWarning
from sklearn.linear_model import LinearR... | bloebp | fb5b4d52606826cd54a0c2436193753ff06c4855 | 2ed7cf4e93e01de4f16ebd2f66af07196aa1065f | Address the typo `creat_` everywhere. | kailashbuki | 263 |
py-why/dowhy | 737 | Add polynom regressor and classifier to gcm | This replaces the ProductRegressor.
Signed-off-by: Patrick Bloebaum <bloebp@amazon.com> | null | 2022-11-01 15:56:18+00:00 | 2022-11-04 17:32:01+00:00 | tests/gcm/test_auto.py | import networkx as nx
import numpy as np
import pandas as pd
from flaky import flaky
from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor
from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, LogisticRegression, RidgeCV
from sklearn.naive_bayes import GaussianNB... | import networkx as nx
import numpy as np
import pandas as pd
from flaky import flaky
from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor
from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, LogisticRegression, RidgeCV
from sklearn.naive_bayes import GaussianNB... | bloebp | fb5b4d52606826cd54a0c2436193753ff06c4855 | 2ed7cf4e93e01de4f16ebd2f66af07196aa1065f | Is there a reason why moved to this generative model? | kailashbuki | 264 |
py-why/dowhy | 737 | Add polynom regressor and classifier to gcm | This replaces the ProductRegressor.
Signed-off-by: Patrick Bloebaum <bloebp@amazon.com> | null | 2022-11-01 15:56:18+00:00 | 2022-11-04 17:32:01+00:00 | tests/gcm/test_auto.py | import networkx as nx
import numpy as np
import pandas as pd
from flaky import flaky
from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor
from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, LogisticRegression, RidgeCV
from sklearn.naive_bayes import GaussianNB... | import networkx as nx
import numpy as np
import pandas as pd
from flaky import flaky
from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor
from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, LogisticRegression, RidgeCV
from sklearn.naive_bayes import GaussianNB... | bloebp | fb5b4d52606826cd54a0c2436193753ff06c4855 | 2ed7cf4e93e01de4f16ebd2f66af07196aa1065f | Wanted to have non-linear data that cannot be capture by a model with polynomial features (here, `X**2` would be captured by it with a degree 2). | bloebp | 265 |
py-why/dowhy | 736 | Add independence test based on the General Covariance Measure | Signed-off-by: Patrick Bloebaum <bloebp@amazon.com> | null | 2022-11-01 01:38:35+00:00 | 2022-11-22 17:51:14+00:00 | dowhy/gcm/independence_test/__init__.py | from .kernel import approx_kernel_based, kernel_based
from .regression import regression_based
def independence_test(X, Y, conditioned_on=None, method="kernel"):
"""Performs a (conditional) independence test.
Three methods for (conditional) independence test are supported at the moment:
* `kernel`: Kerne... | from .general_cov_measure import general_cov_based
from .kernel import approx_kernel_based, kernel_based
from .regression import regression_based
def independence_test(X, Y, conditioned_on=None, method="kernel"):
"""Performs a (conditional) independence test.
Three methods for (conditional) independence test ... | bloebp | d9f27afc18cfec14ffd2e0178f7ba143f409c832 | 099b8c474c35cc3d528be001e8c49fc16643eebc | Typo: Generalised Covariance Measure | kailashbuki | 266 |
py-why/dowhy | 732 | Set seed on data generation for deterministic test | * Set seed for deterministic data generation on `dowhy_function_api.ipynb` notebook.
* Fix wrong parameters on backwards compatibility example.
Fixes #704
Signed-off-by: Andres Morales <andresmor@microsoft.com> | null | 2022-10-31 18:37:31+00:00 | 2022-11-01 15:16:07+00:00 | docs/source/example_notebooks/dowhy_functional_api.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | andresmor-ms | b9bab69d737f34fc32a73feb426d0ddc2f471df2 | 68f5d2b1bc7c5357b243dcef31510cfdc65ff871 | Why comment this stuff out? Should we delete it itstead? | darthtrevino | 267 |
py-why/dowhy | 732 | Set seed on data generation for deterministic test | * Set seed for deterministic data generation on `dowhy_function_api.ipynb` notebook.
* Fix wrong parameters on backwards compatibility example.
Fixes #704
Signed-off-by: Andres Morales <andresmor@microsoft.com> | null | 2022-10-31 18:37:31+00:00 | 2022-11-01 15:16:07+00:00 | docs/source/example_notebooks/dowhy_functional_api.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | andresmor-ms | b9bab69d737f34fc32a73feb426d0ddc2f471df2 | 68f5d2b1bc7c5357b243dcef31510cfdc65ff871 | Those are other examples of executing the same code above, I commented them to avoid making this notebook take more time executing. A user could just copy them to use the API in a slightly different way. But now that I write this, I realize that this also works as test and making sure that it actually works :D I'll unc... | andresmor-ms | 268 |
py-why/dowhy | 732 | Set seed on data generation for deterministic test | * Set seed for deterministic data generation on `dowhy_function_api.ipynb` notebook.
* Fix wrong parameters on backwards compatibility example.
Fixes #704
Signed-off-by: Andres Morales <andresmor@microsoft.com> | null | 2022-10-31 18:37:31+00:00 | 2022-11-01 15:16:07+00:00 | docs/source/example_notebooks/dowhy_functional_api.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | andresmor-ms | b9bab69d737f34fc32a73feb426d0ddc2f471df2 | 68f5d2b1bc7c5357b243dcef31510cfdc65ff871 | Just want to note that we shouldn't use random seeds in unit tests. However, here it's a notebook, so its fine :) | bloebp | 269 |
py-why/dowhy | 732 | Set seed on data generation for deterministic test | * Set seed for deterministic data generation on `dowhy_function_api.ipynb` notebook.
* Fix wrong parameters on backwards compatibility example.
Fixes #704
Signed-off-by: Andres Morales <andresmor@microsoft.com> | null | 2022-10-31 18:37:31+00:00 | 2022-11-01 15:16:07+00:00 | docs/source/example_notebooks/dowhy_functional_api.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | andresmor-ms | b9bab69d737f34fc32a73feb426d0ddc2f471df2 | 68f5d2b1bc7c5357b243dcef31510cfdc65ff871 | We use notebooks as unit tests though, so we should probably disable random seeds in all of them by default. | darthtrevino | 270 |
py-why/dowhy | 727 | Re-introduce include_simulated_confounder as method | Fixes #721
Signed-off-by: Andres Morales <andresmor@microsoft.com> | null | 2022-10-28 22:32:39+00:00 | 2022-10-31 16:28:12+00:00 | dowhy/causal_refuters/add_unobserved_common_cause.py | import copy
import logging
import math
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import scipy.stats
import statsmodels.api as sm
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from tqdm.auto import tqdm
from dowhy.causal... | import copy
import logging
import math
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import scipy.stats
import statsmodels.api as sm
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from tqdm.auto import tqdm
from dowhy.causal... | andresmor-ms | f13ed30f42440552e4a912372abcb7c3023fc9c0 | 18bd1fe5d9941867dbd135e0d2a0af2fb24feea7 | should these constants be named? | darthtrevino | 271 |
py-why/dowhy | 727 | Re-introduce include_simulated_confounder as method | Fixes #721
Signed-off-by: Andres Morales <andresmor@microsoft.com> | null | 2022-10-28 22:32:39+00:00 | 2022-10-31 16:28:12+00:00 | dowhy/causal_refuters/add_unobserved_common_cause.py | import copy
import logging
import math
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import scipy.stats
import statsmodels.api as sm
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from tqdm.auto import tqdm
from dowhy.causal... | import copy
import logging
import math
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import scipy.stats
import statsmodels.api as sm
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from tqdm.auto import tqdm
from dowhy.causal... | andresmor-ms | f13ed30f42440552e4a912372abcb7c3023fc9c0 | 18bd1fe5d9941867dbd135e0d2a0af2fb24feea7 | Added names for these | andresmor-ms | 272 |
py-why/dowhy | 695 | Change mechanics of Docker image build and usage | - Introduce a GH Action to build Docker image for docs generation every time the Dockerfile is changed
- Trigger docs generation when a new Docker image was built
Addresses:
- https://github.com/py-why/dowhy/issues/690
- https://github.com/py-why/dowhy/issues/691 | null | 2022-10-19 15:03:32+00:00 | 2022-10-26 21:10:00+00:00 | docs/Dockerfile | #
# This dockerfile was used to generate darthtrevino/dowhy-docs, used in the
# build-and-publish-docs.yml workflow
#
FROM pego/dowhy-examples-notebooks-deps
RUN apt update
RUN DEBIAN_FRONTEND=noninteractive apt install --yes --quiet curl
RUN curl -sSL https://install.python-poetry.org | python3 - --version 1.2.1
ENV... | #
# This dockerfile was used to generate darthtrevino/dowhy-docs, used in the
# build-and-publish-docs.yml workflow
#
FROM ghcr.io/py-why/dowhy-example-notebooks-deps
RUN apt update
RUN DEBIAN_FRONTEND=noninteractive apt install --yes --quiet curl
RUN curl -sSL https://install.python-poetry.org | python3 - --version ... | petergtz | 9b7e6a74c4dbb7228999998c81d43c5330df9d31 | 3c5cf3a67b054b2baebaae254c31f457e1fd3365 | I'm assuming this comes packed with all the R deps? Do we have a plan for deprecating the R notebooks? | darthtrevino | 273 |
py-why/dowhy | 695 | Change mechanics of Docker image build and usage | - Introduce a GH Action to build Docker image for docs generation every time the Dockerfile is changed
- Trigger docs generation when a new Docker image was built
Addresses:
- https://github.com/py-why/dowhy/issues/690
- https://github.com/py-why/dowhy/issues/691 | null | 2022-10-19 15:03:32+00:00 | 2022-10-26 21:10:00+00:00 | docs/Dockerfile | #
# This dockerfile was used to generate darthtrevino/dowhy-docs, used in the
# build-and-publish-docs.yml workflow
#
FROM pego/dowhy-examples-notebooks-deps
RUN apt update
RUN DEBIAN_FRONTEND=noninteractive apt install --yes --quiet curl
RUN curl -sSL https://install.python-poetry.org | python3 - --version 1.2.1
ENV... | #
# This dockerfile was used to generate darthtrevino/dowhy-docs, used in the
# build-and-publish-docs.yml workflow
#
FROM ghcr.io/py-why/dowhy-example-notebooks-deps
RUN apt update
RUN DEBIAN_FRONTEND=noninteractive apt install --yes --quiet curl
RUN curl -sSL https://install.python-poetry.org | python3 - --version ... | petergtz | 9b7e6a74c4dbb7228999998c81d43c5330df9d31 | 3c5cf3a67b054b2baebaae254c31f457e1fd3365 | > I'm assuming this comes packed with all the R deps?
yes
> Do we have a plan for deprecating the R notebooks?
Yes. I've already removed R dependencies in a couple of notebooks that use the Lalonde dataset and use R only to load it (see recent commit history).
Actually, the plan is not to deprecate the note... | petergtz | 274 |
py-why/dowhy | 693 | Functional api/estimate effect function | #### Estimate Effect function
* Refactors the estimate effect into a separate function to keep backwards compatibility
#### TODO (future PRs):
* Add `fit(...)` method to estimators - Move data related parameters from the constructor to the `fit(...)` method
* Refactor code to avoid `**kwargs` in `__init__(...)` c... | null | 2022-10-18 15:49:21+00:00 | 2022-10-25 17:02:02+00:00 | docs/source/example_notebooks/dowhy_functional_api.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | andresmor-ms | 2044d216c322a4b32c6eadce5da7d83463f19c2f | 05bfa49dacf0061988c96c6f3e3756219df5422a | @andresmor-ms @amit-sharma Hey guys, I was wondering how you think about the following proposal (which is what I believe is what we have also discussed in the past at some point). It might be a bit naiv, because I don't understand all the details and subtleties of the existing implementation. But just throwing it out t... | petergtz | 275 |
py-why/dowhy | 693 | Functional api/estimate effect function | #### Estimate Effect function
* Refactors the estimate effect into a separate function to keep backwards compatibility
#### TODO (future PRs):
* Add `fit(...)` method to estimators - Move data related parameters from the constructor to the `fit(...)` method
* Refactor code to avoid `**kwargs` in `__init__(...)` c... | null | 2022-10-18 15:49:21+00:00 | 2022-10-25 17:02:02+00:00 | docs/source/example_notebooks/dowhy_functional_api.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | andresmor-ms | 2044d216c322a4b32c6eadce5da7d83463f19c2f | 05bfa49dacf0061988c96c6f3e3756219df5422a | Or maybe I'm misunderstanding this, and the whole point of the _function_ `estimate_effect` is to take care of calling the _methods_ `fit` and `estimate_effect`. Then never mind my comment above. | petergtz | 276 |
py-why/dowhy | 693 | Functional api/estimate effect function | #### Estimate Effect function
* Refactors the estimate effect into a separate function to keep backwards compatibility
#### TODO (future PRs):
* Add `fit(...)` method to estimators - Move data related parameters from the constructor to the `fit(...)` method
* Refactor code to avoid `**kwargs` in `__init__(...)` c... | null | 2022-10-18 15:49:21+00:00 | 2022-10-25 17:02:02+00:00 | docs/source/example_notebooks/dowhy_functional_api.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | andresmor-ms | 2044d216c322a4b32c6eadce5da7d83463f19c2f | 05bfa49dacf0061988c96c6f3e3756219df5422a | Hey @petergtz, I believe that what you just described is my end goal here, I think that @amit-sharma still wants to keep the `estimate_effect` function as a way to automatically select parameters (for users that maybe don't know which parameters to pick) I want to separate this into several PRs to avoid creating one gi... | andresmor-ms | 277 |
py-why/dowhy | 693 | Functional api/estimate effect function | #### Estimate Effect function
* Refactors the estimate effect into a separate function to keep backwards compatibility
#### TODO (future PRs):
* Add `fit(...)` method to estimators - Move data related parameters from the constructor to the `fit(...)` method
* Refactor code to avoid `**kwargs` in `__init__(...)` c... | null | 2022-10-18 15:49:21+00:00 | 2022-10-25 17:02:02+00:00 | docs/source/example_notebooks/dowhy_functional_api.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | andresmor-ms | 2044d216c322a4b32c6eadce5da7d83463f19c2f | 05bfa49dacf0061988c96c6f3e3756219df5422a | That's great! Thanks for providing the context, @andresmor-ms. Resolving. | petergtz | 278 |
py-why/dowhy | 693 | Functional api/estimate effect function | #### Estimate Effect function
* Refactors the estimate effect into a separate function to keep backwards compatibility
#### TODO (future PRs):
* Add `fit(...)` method to estimators - Move data related parameters from the constructor to the `fit(...)` method
* Refactor code to avoid `**kwargs` in `__init__(...)` c... | null | 2022-10-18 15:49:21+00:00 | 2022-10-25 17:02:02+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.utils.api import parse_state
class CausalEstimator:
"""Base class for an estimator of causal effect.
Subclasses... | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | andresmor-ms | 2044d216c322a4b32c6eadce5da7d83463f19c2f | 05bfa49dacf0061988c96c6f3e3756219df5422a | estimate_effect does not need the graph as a parameter | amit-sharma | 279 |
py-why/dowhy | 693 | Functional api/estimate effect function | #### Estimate Effect function
* Refactors the estimate effect into a separate function to keep backwards compatibility
#### TODO (future PRs):
* Add `fit(...)` method to estimators - Move data related parameters from the constructor to the `fit(...)` method
* Refactor code to avoid `**kwargs` in `__init__(...)` c... | null | 2022-10-18 15:49:21+00:00 | 2022-10-25 17:02:02+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.utils.api import parse_state
class CausalEstimator:
"""Base class for an estimator of causal effect.
Subclasses... | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | andresmor-ms | 2044d216c322a4b32c6eadce5da7d83463f19c2f | 05bfa49dacf0061988c96c6f3e3756219df5422a | shall we provide the method object here? And get rid of method_kwargs, as done in identification? | amit-sharma | 280 |
py-why/dowhy | 693 | Functional api/estimate effect function | #### Estimate Effect function
* Refactors the estimate effect into a separate function to keep backwards compatibility
#### TODO (future PRs):
* Add `fit(...)` method to estimators - Move data related parameters from the constructor to the `fit(...)` method
* Refactor code to avoid `**kwargs` in `__init__(...)` c... | null | 2022-10-18 15:49:21+00:00 | 2022-10-25 17:02:02+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.utils.api import parse_state
class CausalEstimator:
"""Base class for an estimator of causal effect.
Subclasses... | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | andresmor-ms | 2044d216c322a4b32c6eadce5da7d83463f19c2f | 05bfa49dacf0061988c96c6f3e3756219df5422a | this needs to be modified since causal_estimator is not defined so far. Assuming that the user provides a method object, we can use just use that method object and assign it to `causal_estimator` | amit-sharma | 281 |
py-why/dowhy | 693 | Functional api/estimate effect function | #### Estimate Effect function
* Refactors the estimate effect into a separate function to keep backwards compatibility
#### TODO (future PRs):
* Add `fit(...)` method to estimators - Move data related parameters from the constructor to the `fit(...)` method
* Refactor code to avoid `**kwargs` in `__init__(...)` c... | null | 2022-10-18 15:49:21+00:00 | 2022-10-25 17:02:02+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.utils.api import parse_state
class CausalEstimator:
"""Base class for an estimator of causal effect.
Subclasses... | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | andresmor-ms | 2044d216c322a4b32c6eadce5da7d83463f19c2f | 05bfa49dacf0061988c96c6f3e3756219df5422a | In line 741 we use graph to get the effect modifiers, unless you want them to be provided in parameter without option to get them from the CausalGraph object? | andresmor-ms | 282 |
py-why/dowhy | 693 | Functional api/estimate effect function | #### Estimate Effect function
* Refactors the estimate effect into a separate function to keep backwards compatibility
#### TODO (future PRs):
* Add `fit(...)` method to estimators - Move data related parameters from the constructor to the `fit(...)` method
* Refactor code to avoid `**kwargs` in `__init__(...)` c... | null | 2022-10-18 15:49:21+00:00 | 2022-10-25 17:02:02+00:00 | dowhy/causal_estimator.py | import logging
from collections import namedtuple
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.utils.api import parse_state
class CausalEstimator:
"""Base class for an estimator of causal effect.
Subclasses... | import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy import causal_estimators
from dowhy.causal_graph import CausalGraph
from do... | andresmor-ms | 2044d216c322a4b32c6eadce5da7d83463f19c2f | 05bfa49dacf0061988c96c6f3e3756219df5422a | I'd prefer if we leave it as this and change it when I refactor the actual estimator objects in the next PR | andresmor-ms | 283 |
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