  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.
