Title: A Generalized Framework for the Estimation of Causal Moderation Effects with Randomized Treatments and Non-Randomized Moderators,
Abstract: Researchers are often interested in analyzing conditional treatment effects.
One variant of this is "causal moderation," which implies that intervention
upon a third (moderator) variable would alter the treatment effect. This study
presents a generalized, non-parametric framework for estimating causal
moderation effects given randomized treatments and non-randomized moderators
that achieves a number of goals. First, it highlights how conventional
approaches do not constitute unbiased or consistent estimators of causal
moderation effects. Second, it offers researchers a simple, transparent
approach for estimating causal moderation effects and lays out the assumptions
under which this can be performed consistently and/or without bias. Third, as
part of the estimation process, it allows researchers to implement their
preferred method of covariate adjustment, including parametric and
non-parametric methods, or alternative identification strategies of their
choosing. Fourth, it provides a set-up whereby sensitivity analysis designed
for the average-treatment-effect context can be extended to the moderation
context. An original application is also presented.