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Semiparametric Single-Index Estimation for Average Treatment Effects
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,doi:10.26180/21531639.v1 fatcat:alpbrc2dbffptlljjctlv3kt7q