Sensitivity models and bounds under sequential unmeasured confounding in longitudinal studies
针对纵向研究中随时间变化的治疗和协变量，提出多期敏感性模型来放松序贯无混杂假设，通过凸优化得到总体水平上的尖锐和保守界，为横截面边际敏感性模型提供了首次令人满意的推广。
We consider sensitivity analysis for causal inference in a longitudinal study with time-varying treatments and covariates. It is of interest to assess the worst-case possible values of counterfactual outcome means and average treatment effects under sequential unmeasured confounding. We formulate several multi-period sensitivity models to relax the corresponding versions of the assumption of sequential non-confounding. The primary sensitivity model involves only counterfactual outcomes, whereas the joint and product sensitivity models involve both counterfactual covariates and outcomes. We establish and compare explicit representations for the sharp and conservative bounds at the population level through convex optimization, depending only on the observed data. These results provide for the first time a satisfactory generalization from the marginal sensitivity model in the cross-sectional setting.