Using negative controls to identify causal effects with invalid instrumental variables
本文提出利用阴性对照人群或结局，在工具变量假设被违反时仍能识别因果效应，并开发了多重稳健的局部有效估计量，通过模拟和寿命研究数据验证了其有效性。
Many proposals for the identification of causal effects require an instrumental variable that satisfies strong, untestable unconfoundedness and exclusion restriction assumptions. In this paper, we show how one can potentially identify causal effects under violations of these assumptions by harnessing a negative control population or outcome. This strategy allows one to leverage subpopulations for whom the exposure is degenerate, and requires that the instrument-outcome association satisfies a certain parallel trend condition. We develop semiparametric efficiency theory for a general instrumental variable model, and obtain a multiply robust, locally efficient estimator of the average treatment effect in the treated. The utility of the estimators is demonstrated in simulation studies and an analysis of the Life Span Study.