# Missing Data Evaluation Protocol

The Clarus benchmark includes a missing-observation evaluation suite.

These datasets simulate incomplete monitoring conditions commonly observed in clinical and real-world systems.

## Missing Data Variants

Four missing-data regimes are evaluated.

### Missing t1

The intermediate time point is removed.

Example:

t0 → missing → t2

### Missing t2

The final observation is missing.

Example:

t0 → t1 → missing

### Missing t0

The initial observation is missing.

Example:

missing → t1 → t2

### Random Missing

One or more observations are randomly removed.

## Purpose

These variants evaluate whether models can infer stability from partial trajectories.

This tests robustness to incomplete observation rather than perfect monitoring.

## Evaluation

The prediction task remains unchanged.

Models must produce:

scenario_id,prediction

Evaluation uses the standard Clarus scorer.