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Conditional average treatment effects estimation using Stata

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  • Di Liu

    (StataCorp LLC)

Abstract

Treatment effects estimate the causal effects of a treatment on an outcome. The effect may be heterogeneous. Average treatment effects conditional on a set of variables (CATEs) help us understand heterogeneous treatment effects, and, by construction, are useful to evaluate how different treatment-assignment policies affect different groups in the population. In this talk, I will show how to use Stata's new cate command to answer questions such as the following: Are the treatment effects heterogeneous? How do the treatment effects vary with some variables? Do the treatment effects vary across prespecified groups? Are there unknown groups in the data for which treatment effects differ? Which is best among possible treatment-assignment rules?

Suggested Citation

  • Di Liu, 2025. "Conditional average treatment effects estimation using Stata," Chinese Stata Conference 2025 03, Stata Users Group.
    • Handle: RePEc:boc:chin25:03
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    File URL: http://repec.org/chin2025/China25_Liu.pdf
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    References listed on IDEAS

    as
    1. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    2. X Nie & S Wager, 2021. "Quasi-oracle estimation of heterogeneous treatment effects [TensorFlow: A system for large-scale machine learning]," Biometrika, Biometrika Trust, vol. 108(2), pages 299-319.
    3. Vira Semenova & Victor Chernozhukov, 2021. "Debiased machine learning of conditional average treatment effects and other causal functions," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 264-289.
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