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：我们提出了在未测量暴露-结局混杂因素情形下平均因果效应的新结果。这些结果源于一类估计量（例如医学和公共卫生领域经常关注的指标），而标准平均因果效应方法目前尚未涉及此类估计量。我们认识到这些估计量本质上是关于中介变量平均因果效应的查询。我们以慢性疼痛与阿片类药物处方模式在阿片类药物流行病中的作用研究为切入点，引入这些估计量的概念，并阐明传统方法如何导致无法复现且政策含义模糊的估计结果。我们论证了替代效应具有可复现性与清晰的政策含义，并且可通过经典的前门公式进行非参数识别。作为独立贡献，我们推导出前门公式的新型半参数有效估计量，该估计量具有均匀样本有界性保证。这一性质在同类型既有估计量中独树一帜，我们展示了其在有限样本场景下的优越表现。理论结果已应用于美国国家健康与营养调查数据。