Ignorability for general longitudinal data
本文揭示了可忽略性与通过协变量调整识别因果效应之间的紧密联系，提出稳定性条件作为一般纵向数据中可忽略性的图形化判据，并通过实例说明如何评估该条件。
Likelihood factors that can be disregarded for inference are termed ignorable. We demonstrate that close ties exist between ignorability and identification of causal effects by covariate adjustment. A graphical condition, stability, plays a role analogous to that of missingness at random, but is applicable to general longitudinal data. Our formulation of ignorability does not depend on any notion of missing data, so is appealing in situations where missing data may not actually exist. Several examples illustrate how stability may be assessed.