Unmeasured confounding is a threat to causal inference in observational studies. In recent years, the use of negative controls to mitigate unmeasured confounding has gained increasing recognition and popularity. Negative controls have a long‐standing tradition in laboratory sciences and epidemiology to rule out non‐causal explanations, although they have been used primarily for bias detection. Recently, Miao and colleagues have described sufficient conditions under which a pair of negative control exposure and outcome variables can be used to identify non‐parametrically the average treatment effect (ATE) from observational data subject to uncontrolled confounding. We establish non‐parametric identification of the ATE under weaker conditions in the case of categorical unmeasured confounding and negative control variables. We also provide a general semiparametric framework for obtaining inferences about the ATE while leveraging information about a possibly large number of measured covariates. In particular, we derive the semiparametric efficiency bound in the non‐parametric model, and we propose multiply robust and locally efficient estimators when non‐parametric estimation may not be feasible. We assess the finite sample performance of our methods in extensive simulation studies. Finally, we illustrate our methods with an application to the post‐licensure surveillance of vaccine safety among children.

中文翻译：

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鲁棒的因果推理与双负控制调整相结合，用于绝对不可测的混杂

在观测研究中，不可估量的混杂是因果推理的威胁。近年来，使用消极控制来减轻不可估量的混杂问题已获得越来越多的认可和普及。阴性对照在实验室科学和流行病学中具有悠久的传统，可以排除非因果关系的解释，尽管它们主要用于偏差检测。最近，Miao及其同事描述了足够的条件，在这些条件下，可以使用一对阴性对照暴露和结果变量从不受控制的混杂因素观察数据中非参数地确定平均治疗效果（ATE）。我们在较弱条件下建立了ATE的非参数识别，这是绝对不可测的混杂变量和负控制变量的情况。我们还提供了一个通用的半参数框架，用于获取有关ATE的推论，同时利用有关可能大量的测量协变量的信息。特别是，我们推导了非参数模型中的半参数效率边界，并且当非参数估计不可行时，我们提出了乘稳健估计和局部有效估计。我们在广泛的模拟研究中评估了我们方法的有限样本性能。最后，我们举例说明了我们的方法，并将其应用于对儿童疫苗安全性的许可后监测。当非参数估计可能不可行时，我们提出乘稳健估计和局部有效估计。我们在广泛的模拟研究中评估了我们方法的有限样本性能。最后，我们举例说明了我们的方法，并将其应用于对儿童疫苗安全性的许可后监测。当非参数估计可能不可行时，我们提出乘稳健估计和局部有效估计。我们在广泛的模拟研究中评估了我们方法的有限样本性能。最后，我们举例说明了我们的方法，并将其应用于对儿童疫苗安全性的许可后监测。