Efficient Estimation of Treatment Effects Using Neural Networks with a Diverging Number of Confounders

46 Pages Posted: 4 Nov 2020

See all articles by Xiaohong Chen

Xiaohong Chen

Yale University - Cowles Foundation

Ying

University of California, Riverside (UCR)

Shujie Ma

University of California, Riverside (UCR)

Zheng Zhang

Renmin University of China - Institute of Statistics and Big Data

Multiple version iconThere are 4 versions of this paper

Date Written: September 15, 2020

Abstract

The estimation of causal effects is a primary goal of behavioral, social, economic and biomedical sciences. Under the unconfounded treatment assignment condition, adjustment for confounders requires estimating the nuisance functions relating outcome and/or treatment to confounders. The conventional approaches rely on either a parametric or a nonparametric modeling strategy to approximate the nuisance functions. Parametric methods can introduce serious bias into casual effect estimation due to possible mis-specification, while nonparametric estimation suffers from the ''curse of dimensionality". This paper proposes a new unified approach for efficient estimation of treatment effects using feedforward artificial neural networks (ANN) when the number of covariates is allowed to increase with the sample size. We consider a general optimization framework that includes the average, quantile and asymmetric least squares treatment effects as special cases. Under this unified setup, we develop a generalized optimization estimator for the treatment effect with the nuisance function estimated by ANNs. We further establish the consistency and asymptotic normality of the proposed estimator and show that it attains the semiparametric efficiency bound. The proposed methods are illustrated via simulation studies and a real data application.

Keywords: Treatment effects, Propensity score, Artificial neural networks, Semiparametric efficiency

JEL Classification: C01, C12, C21

Suggested Citation

Chen, Xiaohong and Liu, Ying and Ma, Shujie and Zhang, Zheng, Efficient Estimation of Treatment Effects Using Neural Networks with a Diverging Number of Confounders (September 15, 2020). Available at SSRN: https://ssrn.com/abstract=3693127 or http://dx.doi.org/10.2139/ssrn.3693127

Xiaohong Chen

Yale University - Cowles Foundation ( email )

Box 208281
New Haven, CT 06520-8281
United States

Ying Liu

University of California, Riverside (UCR) ( email )

900 University Avenue
Riverside, CA CA 92521
United States

Shujie Ma

University of California, Riverside (UCR) ( email )

900 University Avenue
Riverside, CA CA 92521
United States

Zheng Zhang (Contact Author)

Renmin University of China - Institute of Statistics and Big Data ( email )

Beijing
China

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