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missing_data_protocol.md

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+# 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.