In observational studies, identification of ATEs is generally achieved by assuming that the correct set of confounders has been measured and properly included in the relevant models. Because this assumption is both strong and untestable, a sensitivity analysis should be performed. Common approaches include modeling the bias directly or varying the propensity scores to probe the effects of a potential unmeasured confounder. In this paper, we take a novel approach whereby the sensitivity parameter is the "proportion of unmeasured confounding:" the proportion of units for whom the treatment is not as good as randomized even after conditioning on the observed covariates. We consider different assumptions on the probability of a unit being unconfounded. In each case, we derive sharp bounds on the average treatment effect as a function of the sensitivity parameter and propose nonparametric estimators that allow flexible covariate adjustment. We also introduce a one-number summary of a study's robustness to the number of confounded units. Finally, we explore finite-sample properties via simulation, and apply the methods to an observational database used to assess the effects of right heart catheterization.
翻译:在观察研究中,通常通过假设正确的混杂者组群已经测量并适当纳入相关模型,来识别ATE,因为这一假设既有力又不可测试,所以应当进行敏感性分析。共同的方法包括直接或对偏差性分数进行模型分析,以调查潜在非计量混杂者的影响。在本文件中,我们采用一种新颖的方法,根据这种方法,敏感度参数是“非计量混杂比例:”治疗效果不如对观察到的共变体进行调节后的随机处理的单位比例。我们考虑了关于一个单位是否无根据的不同假设。在每种情况下,我们从平均处理效果中得出清晰的界限,作为敏感度参数的函数,并提出允许灵活共变式调整的非参数。我们还对一项研究的稳健度与共振单位的数目进行了一行之数的总结。最后,我们通过模拟来探索有限的吸附特性,并将方法应用到用于评估右心导裂影响的观测数据库中。