标题： 在存在潜在变量和选择偏差情况下的总效应识别 显示英文标题

标题： On Identifying Total Effects in the Presence of Latent Variables and Selection bias

摘要： 假设变量之间的因果关系可以描述为一个有向无环图和相应的线性结构方程模型。我们考虑在处理变量和响应变量之间存在潜在变量和选择偏差的情况下总效应的识别问题。Pearl和他的同事提供了后门准则、前门准则（Pearl，2000）和条件工具变量方法（Brito和Pearl，2002）作为存在潜在变量时总效应的可识别性准则，但不适用于存在选择偏差的情况。为了解决这个问题，我们基于可识别因子模型提出了新的图形可识别性准则用于总效应。本文的结果有助于在观察性研究中识别总效应，并为因子模型的识别条件提供了一个新视角。 显示英文摘要

摘要： Assume that cause-effect relationships between variables can be described as a directed acyclic graph and the corresponding linear structural equation model.We consider the identification problem of total effects in the presence of latent variables and selection bias between a treatment variable and a response variable. Pearl and his colleagues provided the back door criterion, the front door criterion (Pearl, 2000) and the conditional instrumental variable method (Brito and Pearl, 2002) as identifiability criteria for total effects in the presence of latent variables, but not in the presence of selection bias. In order to solve this problem, we propose new graphical identifiability criteria for total effects based on the identifiable factor models. The results of this paper are useful to identify total effects in observational studies and provide a new viewpoint to the identification conditions of factor models.