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：我们提出了在暴露-结局存在未测量混杂情况下的平均因果效应新结果。这些结果源于一类目前在平均因果效应标准方法中尚未被关注的估计量，例如在医学和公共卫生领域经常受到关注的估计量。我们将这些估计量识别为探讨干预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中的角色调查为切入点，引入对这些估计量的介绍，并说明传统方法将如何导致不可复现的估计结果及模糊的政策含义。我们认为，我们的替代效应具有可复现性且政策含义明确，此外，这些效应可由经典前门公式非参数识别。作为独立贡献，我们推导出一种新的具备统一有界样本保证的前门公式半参数有效估计量。这一特性在此类现有估计量中独一无二，我们在有限样本情境中展示了其卓越性能。理论结果已通过国家健康与营养调查数据加以应用。