Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules.

Area of Science:

• Machine Learning
• Causal Inference
• Reinforcement Learning

Background:

• Policy learning and evaluation are critical in decision-making processes.
• Existing methods like Kallus (2020) focus on retargeting for efficiency.
• The availability of testing data during training presents new opportunities and challenges.

Purpose of the Study:

• To differentiate the current work from Kallus (2020) based on assumptions and research scope.
• To investigate efficient policy evaluation using auxiliary testing data during training.
• To assess the performance of DRITR (Doubly Robust Importance-Weighted Regression) under varying sample size conditions.

Main Methods:

• Comparative analysis of policy learning frameworks.
• Development and evaluation of efficient value function estimation techniques.
• Assessment of DRITR's performance against efficient policy evaluation methods.

Main Results:

• Assumptions and data variations lead to distinct research problems between this work and Kallus (2020).
• Efficient value function estimates perform competitively when training and testing sample sizes grow comparably.
• DRITR demonstrates robustness and requires less stringent testing sample size conditions compared to other methods.

Conclusions:

• The study clarifies distinctions between related policy learning research.
• Efficient policy evaluation methods are sensitive to the relative growth of training and testing data.
• DRITR offers a flexible and broadly applicable approach to policy evaluation, particularly when testing data is limited.