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Heterogeneous treatment effects and optimal targeting policy evaluation

Author

Listed:
  • Günter J. Hitsch

    (University of Chicago Booth School of Business)

  • Sanjog Misra

    (University of Chicago Booth School of Business)

  • Walter W. Zhang

    (University of Chicago Booth School of Business)

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.

Suggested Citation

  • Günter J. Hitsch & Sanjog Misra & Walter W. Zhang, 2024. "Heterogeneous treatment effects and optimal targeting policy evaluation," Quantitative Marketing and Economics (QME), Springer, vol. 22(2), pages 115-168, June.
  • Handle: RePEc:kap:qmktec:v:22:y:2024:i:2:d:10.1007_s11129-023-09278-5
    DOI: 10.1007/s11129-023-09278-5
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    References listed on IDEAS

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    More about this item

    Keywords

    Targeting; Customer relationship management (CRM); Causal inference; Heterogeneous treatment effects; Machine learning; Field experiments;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D04 - Microeconomics - - General - - - Microeconomic Policy: Formulation; Implementation; Evaluation

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