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Machine Learning for Staggered Difference-in-Differences and Dynamic Treatment Effect Heterogeneity

Author

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  • Julia Hatamyar
  • Noemi Kreif
  • Rudi Rocha
  • Martin Huber

Abstract

We combine two recently proposed nonparametric difference-in-differences methods, extending them to enable the examination of treatment effect heterogeneity in the staggered adoption setting using machine learning. The proposed method, machine learning difference-in-differences (MLDID), allows for estimation of time-varying conditional average treatment effects on the treated, which can be used to conduct detailed inference on drivers of treatment effect heterogeneity. We perform simulations to evaluate the performance of MLDID and find that it accurately identifies the true predictors of treatment effect heterogeneity. We then use MLDID to evaluate the heterogeneous impacts of Brazil's Family Health Program on infant mortality, and find those in poverty and urban locations experienced the impact of the policy more quickly than other subgroups.

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  • Julia Hatamyar & Noemi Kreif & Rudi Rocha & Martin Huber, 2023. "Machine Learning for Staggered Difference-in-Differences and Dynamic Treatment Effect Heterogeneity," Papers 2310.11962, arXiv.org.
    • Handle: RePEc:arx:papers:2310.11962
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    Cited by:

    1. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    2. Gregory Faletto, 2023. "Fused Extended Two-Way Fixed Effects for Difference-in-Differences with Staggered Adoptions," Papers 2312.05985, arXiv.org, revised Apr 2024.

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