The School of Economics invites you to an Econometrics seminar presented by Michael (Qingliang) Fan (WISE, Xiamen University). 

Estimation of Conditional Average Treatment Effects with High-Dimensional Data

Co-authors: 

Yu-Chin Hsu (Chengchi University), Robert P. Lieli (Central European University) and Yichong Zhang (Singapore Management University)

Abstract 

Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the rst 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. This is a key feature since identification is generally more credible if the full vector of conditioning variables, including possible transformations, is high-dimensional.

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.

Download the full paper: Fan_et_al_2019 Estimation of conditional average effects with high-dimensional data