 Estimating causal moderation effects with randomized treatments and non‐randomized moderatorsKirk Bansak Journal of the Royal Statistical Society Series A, 2021, vol. 184, issue 1, 65-86 Abstract: 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. Date: 2021 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssa:v:184:y:2021:i:1:p:65-86 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of the Royal Statistical Society Series A is currently edited by A. Chevalier and L. Sharples More articles in Journal of the Royal Statistical Society Series A from Royal Statistical Society Contact information at EDIRC. |
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