Policy Estimation

Policy estimation, specifically off-policy evaluation (OPE), focuses on assessing the performance of a new policy using data collected under a different policy, crucial for scenarios where direct experimentation is infeasible or costly. Current research emphasizes improving the accuracy and efficiency of OPE estimators, addressing challenges like high variance in importance sampling, bias from model misspecification, and the impact of unobserved confounding. This involves developing novel estimators, such as doubly robust methods and those leveraging policy convolution or generative models, and exploring new evaluation metrics that consider risk-return tradeoffs. The advancements in OPE have significant implications for various fields, including reinforcement learning, recommender systems, and A/B testing, enabling more reliable and efficient decision-making based on offline data.