Recovery and inference of causal effects with sequential adjustment for confounding and attrition
通过序贯调整混杂与样本流失恢复因果效应并开展推断
Confounding bias and selection bias bring two significant challenges to the validity of conclusions drawn from applied causal inference. The latter can stem from informative missingness, such as in cases of attrition. We introduce the Sequential Adjustment Criteria (SAC), which extend available graphical conditions for recovering causal effects from confounding and attrition using sequential regressions, allowing for the inclusion of post-exposure and forbidden variables in…