Policy Learning with Distributional Welfare

TL;DR

This paper proposes a novel approach to treatment allocation that targets distributional welfare using conditional quantiles of treatment effects, offering robustness to model uncertainty and accommodating different policymaker preferences.

Contribution

It introduces a new policy framework based on quantile of treatment effects, with minimax robustness, extending welfare analysis beyond average effects.

Findings

Develops policies targeting distributional welfare using QoTE.

Establishes asymptotic regret bounds for proposed policies.

Provides a flexible framework applicable to various welfare functionals.

Abstract

In this paper, we explore optimal treatment allocation policies that target distributional welfare. Most literature on treatment choice has considered utilitarian welfare based on the conditional average treatment effect (ATE). While average welfare is intuitive, it may yield undesirable allocations especially when individuals are heterogeneous (e.g., with outliers) - the very reason individualized treatments were introduced in the first place. This observation motivates us to propose an optimal policy 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 not generally point-identified. We introduce minimax policies that are robust to this model uncertainty. A range of identifying assumptions can be used to yield more informative policies. For both stochastic and deterministic policies, we establish the asymptotic bound on the regret of implementing the proposed policies. The framework can be generalized to any setting where welfare is defined as a functional of the joint distribution of the potential outcomes.