An Alternative Efficient Estimation of Average Treatment Effects
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Existing studies have obtained efficient estimates of average treatment effects using nonparametric imputation methods that use the inverse of nonparametric propensity score estimates as weights (e.g., Hahn 1998; Hirano, Imbens, and Ridder 2003). In the literature, nonparametric series and logit maximum likelihood series estimations for the propensity score have been proposed. However, in this study, we consider an alternative sieve MLE such as probit, often used in practice, and show the resulting average treatment effect estimates also achieve the semiparametric efficiency bound.
Abstract
Existing studies have obtained efficient estimates of average treatment effects using nonparametric imputation methods that use the inverse of nonparametric propensity score estimates as weights (e.g., Hahn 1998; Hirano, Imbens, and Ridder 2003). In the literature, nonparametric series and logit maximum likelihood series estimations for the propensity score have been proposed. However, in this study, we consider an alternative sieve MLE such as probit, often used in practice, and show the resulting average treatment effect estimates also achieve the semiparametric efficiency bound.
참고문헌 (12)
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