Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning

TL;DR

This paper introduces a flexible nonparametric framework for estimating heterogeneous treatment effects that balances fairness constraints with policy welfare, demonstrating robustness and practical effectiveness through simulations and real-world data.

Contribution

It presents a novel, general approach for fair treatment effect estimation that maintains double robustness and explores the fairness-welfare trade-off in policy learning.

Findings

Estimators exhibit double robustness under standard conditions.

Trade-off between fairness and maximum policy welfare characterized.

Method performs well in simulations and real-world case study.

Abstract

We propose a simple and general framework for nonparametric estimation of heterogeneous treatment effects under fairness constraints. Under standard regularity conditions, we show that the resulting estimators possess the double robustness property. We use this framework to characterize the trade-off between fairness and the maximum welfare achievable by the optimal policy. We evaluate the methods in a simulation study and illustrate them in a real-world case study.