Concept-driven Off Policy Evaluation
Keywords: Off Policy Evaluation, Reinforcement Learning, Interpretability, Concept Bottleneck Models
TL;DR: Concept representations provide better OPE evaluations over state representations while being interpretable.
Abstract: Evaluating off-policy decisions using batch data poses significant challenges due to high variance and limited sample sizes, making reliable evaluation difficult. To improve Off-Policy Evaluation (OPE) performance, we must identify and address the sources of this variance. Recent research on Concept Bottleneck Models (CBMs) shows that using human-explainable concepts can improve predictions and provide better understanding. We propose incorporating concepts into OPE to reduce variance through targeted interventions. Shared disease characteristics, for example, could help identify better treatment options, despite variations in patient vitals. Our work introduces a family of concept-based OPE estimators, proving that they remain unbiased and reduce variance when concepts are known and predefined. Since real-world applications often lack predefined concepts, we further develop an end-to-end algorithm to learn interpretable, concise, and diverse parameterized concepts optimized for variance reduction. Our experiments with synthetic and real-world datasets show that both known and learned concept-based estimators significantly improve OPE performance. Crucially, we show that, unlike other OPE methods, concept-based estimators are easily interpretable and allow for targeted interventions on specific concepts, further enhancing the quality of these estimators.
Primary Area: reinforcement learning
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Submission Number: 10911
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