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• W2967695905 abstract "Recently, there has been great interest in estimating the conditional average treatment effect using flexible machine learning methods. However, in practice, investigators often have working hypotheses about effect heterogeneity across pre-defined subgroups of study units, which we call the groupwise approach. The paper compares two modern ways to estimate groupwise treatment effects, a nonparametric approach and a semiparametric approach, with the goal of better informing practice. Specifically, we compare (a) the underlying assumptions, (b) efficiency and adaption to the underlying data generating models, and (c) a way to combine the two approaches. We also discuss how to test a key assumption concerning the semiparametric estimator and to obtain cluster-robust standard errors if study units in the same subgroups are correlated. We demonstrate our findings by conducting simulation studies and reanalyzing the Early Childhood Longitudinal Study." @default.
• W2967695905 created "2019-08-22" @default.
• W2967695905 creator A5059498826 @default.
• W2967695905 creator A5084062360 @default.
• W2967695905 date "2019-08-12" @default.
• W2967695905 modified "2023-09-25" @default.
• W2967695905 title "A Groupwise Approach for Inferring Heterogeneous Treatment Effects in Causal Inference" @default.
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• W2967695905 doi "https://doi.org/10.48550/arxiv.1908.04427" @default.
• W2967695905 hasPublicationYear "2019" @default.
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