Division of Biostatistics, University of California, Berkeley, California.
Division of Biostatistics, Emory University, Atlanta, Georgia.
Biometrics. 2020 Mar;76(1):109-118. doi: 10.1111/biom.13121. Epub 2019 Oct 30.
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.
许多治疗效果的平均效应的估计量都需要对倾向评分、结果回归或两者进行估计。利用半参数回归或机器学习等灵活技术来估计这些数量通常是有益的。然而，这些回归的最佳估计并不一定导致治疗效果的平均效应的最佳估计，特别是在具有强工具变量的情况下。最近的一项提案通过结果自适应套索（outcome-adaptive lasso）解决了这些问题，这是一种用于估计倾向评分的惩罚回归技术，旨在最小化工具变量对治疗效果估计的影响。然而，这种方法的一个显著限制是其应用仅限于参数模型。我们提出了一种更灵活的替代方法，我们称之为结果高度自适应套索（outcome highly adaptive lasso）。我们讨论了这个估计量的大样本理论，并基于提出的估计量提出了闭式置信区间。我们通过模拟表明，我们的方法优于几种流行的方法。
