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The only thing that can stop bad causal inference is good causal inference

Published online by Cambridge University Press:  13 May 2022

Julia M. Rohrer
Affiliation:
Department of Psychology, Leipzig University, D-04109Leipzig, Germanyjulia.rohrer@uni-leipzig.de; schmukle@uni-leipzig.de; www.juliarohrer.com; https://home.uni-leipzig.de/diffdiag/pppd/?page_id=101
Stefan C. Schmukle
Affiliation:
Department of Psychology, Leipzig University, D-04109Leipzig, Germanyjulia.rohrer@uni-leipzig.de; schmukle@uni-leipzig.de; www.juliarohrer.com; https://home.uni-leipzig.de/diffdiag/pppd/?page_id=101
Richard McElreath
Affiliation:
Department of Human Behavior, Ecology, and Culture, Max Planck Institute for Evolutionary Anthropology, D-04103Leipzig, Germany. richard_mcelreath@eva.mpg.dehttps://xcelab.net/rm/

Abstract

In psychology, causal inference – both the transport from lab estimates to the real world and estimation on the basis of observational data – is often pursued in a casual manner. Underlying assumptions remain unarticulated; potential pitfalls are compiled in post-hoc lists of flaws. The field should move on to coherent frameworks of causal inference and generalizability that have been developed elsewhere.

Type
Open Peer Commentary
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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