Leave No One Undermined: Policy Targeting with Regret Aversion

TL;DR

This paper develops a new method for policy targeting that accounts for regret aversion and treatment heterogeneity, enabling more equitable and efficient decision-making from observational data.

Contribution

It introduces a debiased empirical risk minimization approach for optimal policy learning under regret aversion, with theoretical guarantees and practical applications.

Findings

Achieves a convergence rate of 1/n for excess risk.

Provides upper and lower bounds for the excess risk.

Demonstrates effectiveness on real-world datasets.

Abstract

While the importance of personalized policymaking is widely recognized, fully personalized implementation remains rare in practice, often due to legal, fairness or cost concerns. We study the problem of policy targeting for a regret-averse planner when training data gives a rich set of observables while the assignment rules can only depend on its subset. Our regret-averse criterion reflects a planner's concern about regret inequality across the population. This, in general, leads to a fractional optimal rule due to treatment effect heterogeneity beyond the average treatment effects conditional on the subset of observables. We propose a debiased empirical risk minimization approach to learn the optimal rule from data and establish favorable, new upper and lower bounds for the excess risk, indicating a convergence rate of 1/n and asymptotic efficiency in certain cases. We apply our approach to the National JTPA Study and the International Stroke Trial.