Division of Biostatistics, University of California, Berkeley, CA, USA.
Stat Methods Med Res. 2019 Jun;28(6):1741-1760. doi: 10.1177/0962280218774817. Epub 2018 Jul 11.
The positivity assumption, or the experimental treatment assignment (ETA) assumption, is important for identifiability in causal inference. Even if the positivity assumption holds, practical violations of this assumption may jeopardize the finite sample performance of the causal estimator. One of the consequences of practical violations of the positivity assumption is extreme values in the estimated propensity score (PS). A common practice to address this issue is truncating the PS estimate when constructing PS-based estimators. In this study, we propose a novel adaptive truncation method, Positivity-C-TMLE, based on the collaborative targeted maximum likelihood estimation (C-TMLE) methodology. We demonstrate the outstanding performance of our novel approach in a variety of simulations by comparing it with other commonly studied estimators. Results show that by adaptively truncating the estimated PS with a more targeted objective function, the Positivity-C-TMLE estimator achieves the best performance for both point estimation and confidence interval coverage among all estimators considered.
阳性假设，或实验治疗分配（ETA）假设，对于因果推理中的可识别性很重要。即使阳性假设成立，该假设的实际违反也可能危及因果估计量的有限样本性能。阳性假设实际违反的后果之一是估计倾向评分（PS）中的极值。解决此问题的一种常见做法是在构建基于 PS 的估计器时截断 PS 估计。在这项研究中，我们基于协作靶向最大似然估计（C-TMLE）方法提出了一种新的自适应截断方法，即阳性 C-TMLE。我们通过与其他常用的估计器进行比较，在各种模拟中展示了我们的新方法的出色性能。结果表明，通过使用更有针对性的目标函数自适应地截断估计的 PS，阳性 C-TMLE 估计器在所有考虑的估计器中在点估计和置信区间覆盖方面都取得了最佳性能。
