Confounding bias and selection bias are 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 using sequential regressions, allowing for the inclusion of post-exposure and forbidden variables in the admissible adjustment sets. We propose an estimator for the recovered Average Treatment Effect (ATE) based on Targeted Minimum-Loss Estimation (TMLE), which enjoys multiple robustness under certain conditions. This approach ensures consistency even in scenarios where the Double Inverse Probability Weighting (DIPW) and the na\"ive plug-in sequential regressions approaches fall short. Through a simulation study, we assess the performance of the proposed estimator against alternative methods across different graph setups and model specification scenarios. As a motivating application, we examine the effect of pharmacological treatment for Attention-Deficit/Hyperactivity Disorder (ADHD) upon the scores obtained by diagnosed Norwegian schoolchildren in national tests using observational data ($n=9,352$). Our findings align with the accumulated clinical evidence, affirming a positive but small impact of medication on academic achievement.

翻译：混杂偏倚与选择偏倚是应用因果推断结论有效性的两大挑战，后者可能源于信息性缺失，例如在样本流失的情况下。本文提出序贯调整准则（SAC），该准则扩展了现有基于序贯回归恢复因果效应的图条件，允许在可调整集合中包含暴露后变量与禁止变量。我们基于目标最小损失估计（TMLE）提出一种针对恢复的平均处理效应（ATE）的估计量，该估计量在一定条件下具备多重稳健性。即使在双重逆概率加权（DIPW）与朴素插件序贯回归方法失效的场景下，此方法仍能保证一致性。通过模拟研究，我们在不同图结构设定与模型设定情境下评估了所提估计量相对于其他方法的性能。作为一项动机应用，我们利用观察性数据（$n=9,352$）研究了药物治疗对确诊注意缺陷/多动障碍（ADHD）的挪威学童在国家测试中得分的影响。我们的发现与累积的临床证据一致，证实了药物对学业成就有积极但微弱的影响。