We present new results on average causal effects in settings with unmeasured exposure-outcome confounding. Our results are motivated by a class of estimands, e.g., frequently of interest in medicine and public health, that are currently not targeted by standard approaches for average causal effects. We recognize these estimands as queries about the average causal effect of an intervening variable. We anchor our introduction of these estimands in an investigation of the role of chronic pain and opioid prescription patterns in the opioid epidemic, and illustrate how conventional approaches will lead unreplicable estimates with ambiguous policy implications. We argue that our altenative effects are replicable and have clear policy implications, and furthermore are non-parametrically identified by the classical frontdoor formula. As an independent contribution, we derive a new semiparametric efficient estimator of the frontdoor formula with a uniform sample boundedness guarantee. This property is unique among previously-described estimators in its class, and we demonstrate superior performance in finite-sample settings. Theoretical results are applied with data from the National Health and Nutrition Examination Survey.

翻译：我们提出了存在未测暴露-结局混杂时平均因果效应的新结果。我们的研究源自一类估计量（例如医学和公共卫生领域常见的研究对象），这些估计量目前无法通过标准平均因果效应方法获得。我们将其识别为关于中介变量平均因果效应的查询。以慢性疼痛和类阿片处方模式在阿片类流行病中的角色研究为切入点，我们引入这些估计量，并说明传统方法将导致不可复现的估计结果及模糊的政策含义。我们论证替代效应具有可复现性与清晰政策含义，且可通过经典前门公式进行非参数识别。作为独立贡献，我们推导出具有一致样本有界性保证的前门公式新半参数有效估计量。该特性在同类型已有估计量中具有独特性，并在有限样本场景中展现出更优性能。理论结果已应用于美国国家健康与营养调查数据。