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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 Open the ORCID record for Julia M. Rohrer [Opens in a new window] ,
Stefan C. Schmukle Open the ORCID record for Stefan C. Schmukle [Opens in a new window]  and
Richard McElreath Open the ORCID record for Richard McElreath [Opens in a new window]
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/
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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
Information
Behavioral and Brain Sciences , Volume 45 , 2022 , e91
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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