Model Selection for Treatment Choice: Penalized Welfare Maximization

TL;DR

This paper introduces a penalized decision rule called PWM for treatment assignment, enabling optimal model selection over various covariate subsets and providing theoretical guarantees for regret bounds in policy choice.

Contribution

It extends statistical learning methods to treatment choice, developing the PWM rule with oracle inequalities for model selection and regret bounds.

Findings

PWM performs effective model selection among covariate subsets.

Theoretical regret bounds are established for PWM.

Hold-out procedure formalizes data-driven model selection.

Abstract

This paper studies a penalized statistical decision rule for the treatment assignment problem. Consider the setting of a utilitarian policy maker who must use sample data to allocate a binary treatment to members of a population, based on their observable characteristics. We model this problem as a statistical decision problem where the policy maker must choose a subset of the covariate space to assign to treatment, out of a class of potential subsets. We focus on settings in which the policy maker may want to select amongst a collection of constrained subset classes: examples include choosing the number of covariates over which to perform best-subset selection, and model selection when approximating a complicated class via a sieve. We adapt and extend results from statistical learning to develop the Penalized Welfare Maximization (PWM) rule. We establish an oracle inequality for the regret of the PWM rule which shows that it is able to perform model selection over the collection of available classes. We then use this oracle inequality to derive relevant bounds on maximum regret for PWM. An important consequence of our results is that we are able to formalize model-selection using a "hold-out" procedure, where the policy maker would first estimate various policies using half of the data, and then select the policy which performs the best when evaluated on the other half of the data.