A0431
Title: Policy learning with distributional welfare
Authors: Yifan Cui - Zhejiang University (China) [presenting]
Sukjin Han - University of Bristol (United Kingdom)
Abstract: Optimal treatment allocation policies that target distributional welfare are explored, and an optimal policy is proposed that allocates the treatment based on the conditional quantile of individual treatment effects (QoTE). Depending on the choice of the quantile probability, this criterion can accommodate a policymaker who is either prudent or negligent. The challenge of identifying the QoTE lies in its requirement for knowledge of the joint distribution of the counterfactual outcomes, which is generally hard to recover even with experimental data. Therefore, minimax policies are introduced that are robust to model uncertainty. A range of identifying assumptions can be used to yield more informative policies. For both stochastic and deterministic policies, the asymptotic bound is established on the regret of implementing the proposed policies. In simulations and two empirical applications, optimal decisions based on the QoTE are compared with decisions based on other criteria. The framework can be generalized to any setting where welfare is defined as a functional part of the joint distribution of potential outcomes.
