论文标题：Estimation of Average Treatment Effect Based on A Semiparametric Propensity Score

　　发表时间：2020.06接收

　　论文所有作者：Yu Sun, Karen X.Yan, Qi Li

　　期刊名及所属分类：Econometric Reviews(国际B)

　　英文摘要：Many estimators of the average effect of a treatment on an outcome require estimation of the propensity score, the outcome regression, or both. It is often beneficial to utilize flexible techniques, such as semiparametric regression or machine learning, to estimate these quantities. However, optimal estimation of these regressions does not necessarily lead to optimal estimation of the average treatment effect, particularly in settings with strong instrumental variables. A recent proposal addressed these issues via the outcome‐adaptive lasso, a penalized regression technique for estimating the propensity score that seeks to minimize the impact of instrumental variables on treatment effect estimators. However, a notable limitation of this approach is that its application is restricted to parametric models. We propose a more flexible alternative that we call the outcome highly adaptive lasso. We discuss the large sample theory for this estimator and propose closed‐form confidence intervals based on the proposed estimator. We show via simulation that our method offers benefits over several popular approaches.

　　中文摘要：许多估计治疗对结果的平均影响的人需要估计倾向评分，结果回归，或两者兼而有之。利用灵活的技术，如半参数回归或机器学习，来估计这些数量通常是有益的。然而，对这些回归的最佳估计并不一定导致对平均治疗效果的最佳估计，特别是在具有强大工具变量的环境中。最近的一项提案通过结果自适应lasso来解决这些问题，lasso是一种用于估计倾向得分的惩罚回归技术，旨在将工具变量对治疗效果评估器的影响最小化。然而，这个方法的一个显著的局限性是它的应用仅限于参数模型。我们提出了一个更灵活的选择，我们称之为结果高度适应套索。讨论了该估计量的大样本理论，并在此基础上提出了闭型置信区间。我们通过模拟表明，我们的方法比几种流行的方法更有优势。