Semiparametric Single-Index Estimation for Average Treatment Effects

Difang Huang, Jiti Gao () and Tatsushi Oka

No 10/22, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: We propose a semiparametric method to estimate the average treatment effect under the assumption of unconfoundedness given observational data. Our estimation method alleviates misspecification issues of the propensity score function by estimating the single-index link function involved through Hermite polynomials. Our approach is computationally tractable and allows for moderately large dimension covariates. We provide the large sample properties of the estimator and show its validity. Also, the average treatment effect estimator achieves the parametric rate and asymptotic normality. Our extensive Monte Carlo study shows that the proposed estimator is valid in finite samples. We also provide an empirical analysis on the effect of maternal smoking on babies' birth weight and the effect of job training program on future earnings.

Keywords: Average treatment effects; causal inference; Hermite series expansion; propensity score (search for similar items in EconPapers)
JEL-codes: C14 C21 C31 (search for similar items in EconPapers)
Pages: 64
Date: 2022
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Working Paper: Semiparametric Single-Index Estimation for Average Treatment Effects (2024)
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