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Assessing Heterogeneity of Treatment Effects

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arxiv 2306.15048 v4 pith:FDDQPKOR submitted 2023-06-26 econ.EM stat.APstat.ME

classification econ.EMstat.APstat.ME
keywords treatmenteffectsheterogeneouspoorwhenabsenceapplicationsassessing
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Heterogeneous treatment effects are of major interest in economics. For example, a poverty reduction measure would be best evaluated by its effects on those who would be poor in the absence of the treatment, or by the share among the poor who would increase their earnings because of the treatment. While these quantities are not identified, we derive nonparametrically sharp bounds using only the marginal distributions of the control and treated outcomes. Applications to microfinance and welfare reform demonstrate their utility even when the average treatment effects are not significant and when economic theory makes opposite predictions between heterogeneous individuals.

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  1. Semiparametric Inference for Partially Identifiable Data Fusion Estimands via Double Machine Learning

    stat.ME 2025-02 conditional novelty 6.0 of 10

    A double machine learning procedure estimates outer bounds for data-fusion estimands using only conditional means and variances, with proven asymptotic normality and semiparametric efficiency.

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