Estimation of Conditional Average Treatment Effects with High-Dimensional Data
Robert Lieli and
Papers from arXiv.org
Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. Conditioning on a large number of variables (including their possible transformations) generally enhances the plausibility of the unconfoundedness assumption. The second stage consists of a low-dimensional kernel regression, reducing CATE to a function of the covariate(s) of interest. We consider two variants of the estimator depending on whether the nuisance functions are estimated over the full sample or over a hold-out sample. Building on Belloni at al. (2017) and Chernozhukov et al. (2018), we derive functional limit theory for the estimators and provide an easy-to-implement procedure for uniform inference based on the multiplier bootstrap. The empirical application revisits the effect of maternal smoking on a baby's birth weight as a function of the mother's age.
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Date: 2019-08, Revised 2019-10
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1908.02399
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