Stanghellini Elena, Doretti Marco, Tezuka Taiki
Department of Economics, University of Perugia, 06100 Perugia, Italy.
Department of Statistics, Computer Science, and Applications, University of Florence, 50134 Florence, Italy.
Am J Epidemiol. 2025 Mar 4;194(3):562-564. doi: 10.1093/aje/kwae337.
This short note is a commentary on a 2024 article by Mathur and Shpitser in the Journal, with the aim to enlarge the class of graphs for which the conditional average treatment effect is nonparametrically identified, by allowing the outcome to be on the pathway between the treatment and the selection indicator. A first straightforward generalization is possible when (1) the outcome $Y$ is binary, and (2) the population prevalence of $Y$ is known a priori or can be made the object of a sensitivity analysis. Furthermore, identification of the effect is possible also for $Y$ having any nature, provided that a selection bias breaking node $V$ exists and the population prevalence of $V$ is known.
本短文是对Mathur和Shpitser于2024年发表在该期刊上一篇文章的评论，目的是通过允许结果处于治疗与选择指标之间的路径上，扩大条件平均治疗效应可进行非参数识别的图的类别。当（1）结果Y为二元变量，且（2）Y的总体患病率是先验已知的或可作为敏感性分析的对象时，第一种直接的推广是可行的。此外，只要存在一个打破选择偏倚的节点V且V的总体患病率已知，对于任何性质的Y，效应的识别也是可能的。
