Estimation of average treatment effect based on a semiparametric propensity score
提出用半参数单指标模型估计倾向得分，避免非参数方法的维度灾难，并给出平均处理效应估计量的渐近分布。蒙特卡洛模拟显示该方法优于传统非参数核方法，并应用于右心导管术对医疗效果影响的实证分析。
This paper considers the estimation of average treatment effect using propensity score method. We propose to use a semiparametric single-index model to estimate the propensity score. This avoids the curse of dimensionality problem with the nonparametric method based propensity score estimator. We establish the asymptotic distribution of the average treatment effect estimator. Monte Carlo simulation results show that the proposed method works well in finite samples and outperforms the conventional nonparametric kernel approach. We apply the proposed method to an empirical data examining the efficacy of right heart catheterization on medical outcomes.