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 sup-populations for whom the exposure is degenerate, and requires that the instrument-outcome association satisfies a certain parallel trend condition. We develop the 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.

翻译：许多识别因果效应的方案要求工具变量满足强且不可检验的无混杂性和排他性约束假设。本文展示了如何通过利用负对照群体或结果，在这些假设被违反时仍可能识别因果效应。该策略允许研究者利用暴露呈退化状态的亚群体，并要求工具变量与结果之间的关联满足特定的平行趋势条件。我们为广义工具变量模型发展了半参数效率理论，并获得了处理组平均处理效应的多重稳健且局部有效的估计量。通过模拟研究和寿命研究分析，验证了所提估计量的实用性。