Shrinkage Methods for Treatment Choice

TL;DR

This paper introduces a shrinkage-based decision rule for treatment choice that minimizes maximum regret, demonstrating improved performance and robustness over existing methods through theoretical analysis and empirical data application.

Contribution

It proposes a new computationally feasible shrinkage rule for treatment decisions that outperforms traditional methods in regret minimization and robustness.

Findings

Shrinkage rule can have smaller maximum regret than CES and pooling rules.

The proposed method is robust to misspecification of CATEs.

Numerical experiments support theoretical advantages.

Abstract

This study examines the problem of determining whether to treat individuals based on observed covariates. The most common decision rule is the conditional empirical success (CES) rule proposed by Manski (2004), which assigns individuals to treatments that yield the best experimental outcomes conditional on the observed covariates. Conversely, using shrinkage estimators, which shrink unbiased but noisy preliminary estimates toward the average of these estimates, is a common approach in statistical estimation problems because it is well-known that shrinkage estimators may have smaller mean squared errors than unshrunk estimators. Inspired by this idea, we propose a computationally tractable shrinkage rule that selects the shrinkage factor by minimizing an upper bound of the maximum regret. Then, we compare the maximum regret of the proposed shrinkage rule with those of the CES and pooling rules when the space of conditional average treatment effects (CATEs) is correctly specified or misspecified. Our theoretical results demonstrate that the shrinkage rule performs well in many cases and these findings are further supported by numerical experiments. Specifically, we show that the maximum regret of the shrinkage rule can be strictly smaller than those of the CES and pooling rules in certain cases when the space of CATEs is correctly specified. In addition, we find that the shrinkage rule is robust against misspecification of the space of CATEs. Finally, we apply our method to experimental data from the National Job Training Partnership Act Study.