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Estimation of Heterogeneous Individual Treatment Effects With Endogenous Treatments

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  • Qian Feng
  • Quang Vuong
  • Haiqing Xu

Abstract

This article estimates individual treatment effects (ITE) and its probability distribution in a triangular model with binary-valued endogenous treatments. Our estimation procedure takes two steps. First, we estimate the counterfactual outcome and hence, the ITE for every observational unit in the sample. Second, we estimate the ITE density function of the whole population. Our estimation method does not suffer from the ill-posed inverse problem associated with inverting a nonlinear functional. Asymptotic properties of the proposed method are established. We study its finite sample properties in Monte Carlo experiments. We also illustrate our approach with an empirical application assessing the effects of 401(k) retirement programs on personal savings. Our results show that there exists a small but statistically significant proportion of individuals who experience negative effects, although the majority of ITEs is positive. Supplementary materials for this article are available online.

Suggested Citation

  • Qian Feng & Quang Vuong & Haiqing Xu, 2020. "Estimation of Heterogeneous Individual Treatment Effects With Endogenous Treatments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 231-240, January.
    • Handle: RePEc:taf:jnlasa:v:115:y:2020:i:529:p:231-240
    DOI: 10.1080/01621459.2018.1543121
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    Cited by:

    1. Koki Fusejima, 2020. "Identification of multi-valued treatment effects with unobserved heterogeneity," Papers 2010.04385, arXiv.org, revised Apr 2023.
    2. Bruneel-Zupanc, Christophe Alain, 2021. "Discrete-Continuous Dynamic Choice Models: Identification and Conditional Choice Probability Estimation," TSE Working Papers 21-1185, Toulouse School of Economics (TSE).
    3. Christopher Adjaho & Timothy Christensen, 2022. "Externally Valid Policy Choice," Papers 2205.05561, arXiv.org, revised Jul 2023.
    4. Fusejima, Koki, 2024. "Identification of multi-valued treatment effects with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 238(1).
    5. Abrevaya, Jason & Xu, Haiqing, 2023. "Estimation of treatment effects under endogenous heteroskedasticity," Journal of Econometrics, Elsevier, vol. 234(2), pages 451-478.
    6. Feng, Junlong, 2024. "Matching points: Supplementing instruments with covariates in triangular models," Journal of Econometrics, Elsevier, vol. 238(1).

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