Counterfactual Evaluation

Counterfactual evaluation asks whether a system would change its prediction, explanation, or action appropriately when relevant evidence or conditions change, rather than merely measuring performance on observed cases. Common approaches use paired interventions and benchmarks, simulator or world-model rollouts, controlled replay, and causal or off-policy estimators to compare factual and alternative outcomes. These methods are applied to language and vision models, clinical decisions, recommender systems, and interactive agents, where ordinary accuracy may hide failures in evidence use, safety, or responsiveness. A central challenge is that alternative outcomes are usually unobserved, so conclusions depend on assumptions about causal structure, simulator fidelity, intervention realism, and which metrics reflect useful behavior.