Estimation of Conditional Average Treatment Effects With High-Dimensional Data

By Qingliang Fan, Yu‐Chin Hsu, Robert P. Lieli, Yichong Zhang. 2019

135 citations recorded by the source corpus.

Abstract 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. The second stage consists of a low-dimensional local linear 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. and Chernozhukov et al., 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.

Subfields: Statistics and Probability

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Citations in the corpus

Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, background): “Independently and contemporaneously to the present version of the paper, Fan et al. (2019) and Zimmert and Lechner (2019) define and study perfectly localized average treatment effects with high-dimensional confounders.”
Towards optimal doubly robust estimation of heterogeneous causal effects (2020, background): “Fan et al. [2019] also studied a local-constant variant, but did not consider the case where the CATE is smoother than the regression functions.”