Identification of Causal Mechanisms Based on Between-Subject Double Randomization Designs

Wunsch, Conny; Strobl, Renate

doi:10.2139/ssrn.3209723

Cited by 2 publications

(1 citation statement)

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“…claims involves chaining them together "piece by piece," for example, by running multiple experiments. Combining such independent estimates still requires substantial assumptions (e.g., Imai et al, 2011, p. 770), although they may often be more defensible (Pirlott & MacKinnon, 2016;Strobl & Wunsch, 2018). At the same time, this of course presumes that the mediator can be manipulated in a targeted manner, which may not always be the case, in particular if it is a psychological variable (Eronen, 2020).…”

Section: Mediation: Double Troublementioning

confidence: 99%

That’s a Lot to Process! Pitfalls of Popular Path Models

Rohrer

Hünermund

Arslan

et al. 2022

Advances in Methods and Practices in Psychological Science

116

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Path models to test claims about mediation and moderation are a staple of psychology. But applied researchers may sometimes not understand the underlying causal inference problems and thus endorse conclusions that rest on unrealistic assumptions. In this article, we aim to provide a clear explanation for the limited conditions under which standard procedures for mediation and moderation analysis can succeed. We discuss why reversing arrows or comparing model fit indices cannot tell us which model is the right one and how tests of conditional independence can at least tell us where our model goes wrong. Causal modeling practices in psychology are far from optimal but may be kept alive by domain norms that demand every article makes some novel claim about processes and boundary conditions. We end with a vision for a different research culture in which causal inference is pursued in a much slower, more deliberate, and collaborative manner.

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Section: Mediation: Double Troublementioning

confidence: 99%