Causal mediation with instrumental variables
Kara E. Rudolph, Nicholas Williams, Ivan Diaz

TL;DR
This paper develops new instrumental variable methods for causal mediation analysis, addressing unobserved confounding in both exposure and mediator-outcome relationships, with applications demonstrated in a housing voucher experiment.
Contribution
It introduces novel estimands and robust estimators for IV-based mediation analysis under unobserved confounding, extending existing causal inference frameworks.
Findings
New estimands for IV-mediated effects with one or two IVs
Robust, nonparametric estimators for these effects
Application to a housing voucher experiment demonstrating practical utility
Abstract
Mediation analysis is a strategy for understanding the mechanisms by which treatments or interventions affect later outcomes. Mediation analysis is frequently applied in randomized trial settings, but typically assumes: a) that randomized assignment is the exposure of interest as opposed to actual take-up of the intervention, and b) no unobserved confounding of the mediator-outcome relationship. In contrast to the rich literature on instrumental variable (IV) methods to estimate a total effect of a non-randomized exposure, there has been almost no research into using IV as an identification strategy in the presence of both exposure-outcome and mediator-outcome unobserved confounding. In response, we define and identify novel estimands -- complier interventional direct and indirect effects (i.e., IV mediational effects) in two scenarios: 1) with a single IV for the exposure, and 2) with…
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Statistical Methods and Bayesian Inference
