Causal graphs may inform covariate adjustment for estimating causal effects and improve estimation efficiency by exploiting the graphical structure. In many applications, however, the target causal parameter may not be point-identified due to the presence of unmeasured confounding. Sensitivity analysis methods address this challenge by characterizing bounds on the causal parameter under varying assumptions about the magnitude or form of unmeasured confounding. We focus on semiparametric efficient estimation of causal effects in non-identifiable settings, assuming a known (or hypothesized) causal graph. We propose an influence function projection approach that exploits the conditional independence constraints implied by the graph to improve the efficiency of semiparametric estimators of upper and lower bounds on the average causal effect under a given sensitivity analysis model. Our approach applies across multiple sensitivity analysis frameworks and causal estimands, thereby connecting knowledge of graphical structure with the sensitivity analysis literature. We illustrate our approach through simulations and real data examples thought to be affected by unmeasured confounding, including the effect of labor training program on post-intervention earnings, and the effect of low ejection fraction on heart failure death.

翻译：因果图可以通过利用图形结构为协变量调整提供指导，以估计因果效应并提高估计效率。然而，在许多应用中，由于存在未测量混杂，目标因果参数可能无法被点识别。敏感性分析方法通过在不同假设下（关于未测量混杂的强度或形式）刻画因果参数的界限来应对这一挑战。本文聚焦于非可识别场景下半参数高效估计因果效应，假定已知（或假设的）因果图。我们提出了一种影响函数投影方法，该方法利用因果图蕴含的条件独立性约束，在给定敏感性分析模型下，提高平均因果效应上下界半参数估计量的效率。我们的方法适用于多种敏感性分析框架和因果估计量，从而将图结构知识与敏感性分析文献联系起来。我们通过模拟和真实数据示例（包括劳动培训项目对干预后收入的影响、低射血分数对心力衰竭死亡的影响）展示了所提出的方法，这些案例被认为受到未测量混杂的影响。