Concept-driven Off Policy Evaluation

• URL: http://arxiv.org/abs/2411.19395v1
• Date: Thu, 28 Nov 2024 22:15:06 GMT
• Title: Concept-driven Off Policy Evaluation
• Authors: Ritam Majumdar, Jack Teversham, Sonali Parbhoo,
• Abstract summary: We develop a family of concept-based OPE estimators, proving that they remain unbiased and reduce variance when concepts are known and predefined. Experiments with synthetic and real-world datasets show that both known and learned concept-based estimators significantly improve OPE performance. 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.
• Score: 2.789652596206117
• License:
• Abstract: Evaluating off-policy decisions using batch data poses significant challenges due to limited sample sizes leading to high variance. To improve Off-Policy Evaluation (OPE), 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. 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.

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