Efficient and flexible estimation of natural mediation effects under intermediate confounding and monotonicity constraints
Kara E. Rudolph, Ivan Diaz

TL;DR
This paper develops an efficient, nonparametric estimator for natural mediation effects under intermediate confounding with monotonicity assumptions, demonstrated through simulations and real data analysis.
Contribution
It introduces a novel multiply robust estimator for natural direct and indirect effects under intermediate confounding with monotonicity, enhancing causal inference methods.
Findings
Estimator shows good finite sample performance in simulations.
Application to housing voucher data reveals mediation effects on adolescent mental health.
Method extends causal mediation analysis under complex confounding scenarios.
Abstract
Natural direct and indirect effects are mediational estimands that decompose the average treatment effect and describe how outcomes would be affected by contrasting levels of a treatment through changes induced in mediator values (in the case of the indirect effect) or not through induced changes in the mediator values (in the case of the direct effect). Natural direct and indirect effects are not generally point-identifiable in the presence of a treatment-induced confounder, however they may still be identified if one is willing to assume monotonicity between a treatment and the treatment-induced confounder. We argue that this assumption may be reasonable in the relatively common encouragement-design trial setting where intervention is randomized treatment assignment and the treatment-induced confounder is whether or not treatment was actually taken/adhered to. We develop efficiency…
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Health Systems, Economic Evaluations, Quality of Life · Statistical Methods and Bayesian Inference
