Estimating causal moderation effects with randomized treatments and non-randomized moderators

Researchers are often interested in analysing 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 considers the conditions under which causal moderation can be identified and presents a generalized framework for estimating causal moderation effects given randomized treatments and non-randomized moderators. 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. In addition, it provides a set-up whereby sensitivity analysis designed for the average treatment effect context can be extended to the moderation context. To illustrate the methods, the study presents two applications: one dealing with the effect of using the term 'welfare' to describe public assistance in the United States, and one dealing with the effect of asylum seekers' religion on European attitudes towards asylum seekers.

Automated summary

This study examines the conditions for identifying causal moderation and presents a framework for estimating causal moderation effects. Researchers can implement their preferred method of covariate adjustment and extend sensitivity analysis to the moderation context.