Robust inference on population indirect causal effects: the generalized front-door criterion
Isabel R. Fulcher, Ilya Shpitser, Stella Marealle, Eric J. Tchetgen, Tchetgen

TL;DR
This paper introduces the population intervention indirect effect (PIIE), a new causal measure that can be identified even with unmeasured confounding, extending Pearl's front-door criterion and providing robust inference methods.
Contribution
It proposes the PIIE, a novel indirect effect measure that relaxes no unmeasured confounding assumptions and generalizes existing causal identification criteria.
Findings
PIIE can be nonparametrically identified with unmeasured confounding.
Develops doubly robust semiparametric estimators for PIIE.
Applied methods to assess monetary saving interventions in Tanzania.
Abstract
Standard methods for inference about direct and indirect effects require stringent no unmeasured confounding assumptions which often fail to hold in practice, particularly in observational studies. The goal of this paper is to introduce a new form of indirect effect, the population intervention indirect effect (PIIE), that can be nonparametrically identified in the presence of an unmeasured common cause of exposure and outcome. This new type of indirect effect captures the extent to which the effect of exposure is mediated by an intermediate variable under an intervention that holds the component of exposure directly influencing the outcome at its observed value. The PIIE is in fact the indirect component of the population intervention effect, introduced by Hubbard and Van der Laan (2008). Interestingly, our identification criterion generalizes Judea Pearl's front-door criterion as it…
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