Estimation of Heterogeneous Individual Treatment Effects With Endogenous Treatments

Qian Feng, Quang Vuong, Haiqing Xu

    Research output: Contribution to journalArticlepeer-review


    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.

    Original languageEnglish (US)
    Pages (from-to)231-240
    Number of pages10
    JournalJournal of the American Statistical Association
    Issue number529
    StatePublished - Jan 2 2020


    • 401(k) retirement programs
    • Binary endogenous variable
    • Counterfactual mapping
    • Individual treatment effects
    • Nonseparable triangular models

    ASJC Scopus subject areas

    • Statistics and Probability
    • Statistics, Probability and Uncertainty


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