Heterogeneous Treatment Effects and Optimal Targeting Policy Evaluation

53 Pages Posted: 6 Feb 2018 Last revised: 7 Dec 2023

See all articles by Günter J. Hitsch

Günter J. Hitsch

University of Chicago - Booth School of Business

Sanjog Misra

University of Chicago - Booth School of Business

Walter Zhang

University of Chicago - Booth School of Business

Date Written: November 6, 2023

Abstract

Abstract We present a general framework to target customers using optimal targeting policies, and we document the profit differences from alternative estimates of the optimal targeting policies. Two foundations of the framework are conditional average treatment effects (CATEs) and off-policy evaluation using data with randomized targeting. This policy evaluation approach allows us to evaluate an arbitrary number of different targeting policies using only one randomized data set and thus provides large cost advantages over conducting a corresponding number of field experiments. We use different CATE estimation methods to construct and compare alternative targeting policies. Our particular focus is on the distinction between indirect and direct methods. The indirect methods predict the CATEs using a conditional expectation function estimated on outcome levels, whereas the direct methods specifically predict the treatment effects of targeting. We introduce a new direct estimation method called treatment effect projection (TEP). The TEP is a non-parametric CATE estimator that we regularize using a transformed outcome loss which, in expectation, is identical to a loss that we could construct if the individual treatment effects were observed. The empirical application is to a catalog mailing with a high-dimensional set of customer features. We document the profits of the estimated policies using data from two campaigns conducted one year apart, which allows us to assess the transportability of the predictions to a campaign implemented one year after collecting the training data. All estimates of the optimal targeting policies yield larger profits than uniform policies that target none or all customers. Further, there are significant profit differences across the methods, with the direct estimation methods yielding substantially larger economic value than the indirect methods.

Keywords: Targeting, customer relationship management (CRM), causal inference, heterogeneous treatment effects, machine learning, field experiments

Suggested Citation

Hitsch, Guenter J. and Misra, Sanjog and Zhang, Walter, Heterogeneous Treatment Effects and Optimal Targeting Policy Evaluation (November 6, 2023). Available at SSRN: https://ssrn.com/abstract=3111957 or http://dx.doi.org/10.2139/ssrn.3111957

Guenter J. Hitsch (Contact Author)

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

Sanjog Misra

University of Chicago - Booth School of Business ( email )

5807 South Woodlawn Avenue
Chicago, IL 60637
United States

Walter Zhang

University of Chicago - Booth School of Business ( email )

5807 S Woodlawn Ave
Chicago, IL 60637
United States

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