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The Use of Machine Learning in Treatment Effect Estimation

In: Econometrics with Machine Learning

Author

Listed:
  • Robert P. Lieli

    (Central European University)

  • Yu-Chin Hsu

    (Academia Sinica
    National Central University and National Chengchi University)

  • Ágoston Reguly

    (Central European University)

Abstract

Treatment effect estimation from observational data relies on auxiliary prediction exercises. This chapter presents recent developments in the econometrics literature showing that machine learning methods can be fruitfully applied for this purpose. The double machine learning (DML) approach is concerned primarily with selecting the relevant control variables and functional forms necessary for the consistent estimation of an average treatment effect. We explain why the use of orthogonal moment conditions is crucial in this setting. Another, somewhat distinct, strand of the literature focuses on treatment effect heterogeneity through the discovery of the conditional average treatment effect (CATE) function. Here we distinguish between methods aimed at estimating the entire function and those that project it on a pre-specified coordinate. We also present an empirical application that illustrates some of the methods.

Suggested Citation

  • Robert P. Lieli & Yu-Chin Hsu & Ágoston Reguly, 2022. "The Use of Machine Learning in Treatment Effect Estimation," Advanced Studies in Theoretical and Applied Econometrics, in: Felix Chan & László Mátyás (ed.), Econometrics with Machine Learning, chapter 0, pages 79-109, Springer.
    • Handle: RePEc:spr:adschp:978-3-031-15149-1_3
    DOI: 10.1007/978-3-031-15149-1_3
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