Counterfactual Treatment Effects: Estimation and Inference

Yu Chin Hsu, Tsung Chih Lai*, Robert P. Lieli

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    Abstract (may include machine translation)

    This article proposes statistical methods to evaluate the quantile counterfactual treatment effect (QCTE) if one were to change the composition of the population targeted by a status quo program. QCTE enables a researcher to carry out an ex-ante assessment of the distributional impact of certain policy interventions or to investigate the possible explanations for treatment effect heterogeneity. Assuming unconfoundedness and invariance of the conditional distributions of the potential outcomes, QCTE is identified and can be nonparametrically estimated by a kernel-based method. Viewed as a random function over the continuum of quantile indices, the estimator converges weakly to a zero mean Gaussian process at the parametric rate. We propose a multiplier bootstrap procedure to construct uniform confidence bands, and provide similar results for average effects and for the counterfactually treated subpopulation. We also present Monte Carlo simulations and two counterfactual exercises that provide insight into the heterogeneous earnings effects of the Job Corps training program in the United States.

    Original languageEnglish
    Pages (from-to)240-255
    Number of pages16
    JournalJournal of Business and Economic Statistics
    Volume40
    Issue number1
    DOIs
    StatePublished - 2022

    Keywords

    • Counterfactual analysis
    • Job Corps
    • Multiplier bootstrap
    • Program evaluation

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