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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, [email protected]; [email protected]; www.juliarohrer.com; https://home.uni-leipzig.de/diffdiag/pppd/?page_id=101
Stefan C. Schmukle
Affiliation:
Department of Psychology, Leipzig University, D-04109Leipzig, [email protected]; [email protected]; 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. [email protected]://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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References

Fryer, R. G. (2019). An empirical analysis of racial differences in police use of force. The Journal of Political Economy, 127(3), 1210–1261.CrossRefGoogle Scholar
Gelman, A., & Imbens, G. (2013). Why ask why? Forward causal inference and reverse causal questions (No. w19614). National Bureau of Economic Research. https://doi.org/10.3386/w19614.Google Scholar
Grosz, M. P., Rohrer, J. M., & Thoemmes, F. (2020). The taboo against explicit causal inference in nonexperimental psychology. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 15(5), 1243–1255.CrossRefGoogle ScholarPubMed
Hernán, M. A., & Robins, J. M. (2010). Causal inference: What If. CRC. https://grass.upc.edu/en/seminar/presentation-files/causal-inference/chapters-1-i-2/@@download/file/BookHernanRobinsCap1_2.pdf.Google Scholar
Hu, L. (2021). Law, liberation, and causal inference. LPE Project. https://lpeproject.org/blog/law-liberation-and-causal-inference/.Google Scholar
Hünermund, P. (2018). Nonlinear Mediation Analysis. https://p-hunermund.com/2018/01/17/nonlinear-mediation-analysis/.Google Scholar
Kohler-Hausmann, I. (2018). Eddie Murphy and the dangers of counterfactual causal thinking about detecting racial discrimination. Nw. UL Rev. https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/illlr113&section=38.Google Scholar
Krieger, N., & Smith, G. D. (2016). The tale wagged by the DAG: Broadening the scope of causal inference and explanation for epidemiology. International Journal of Epidemiology, 45(6), 1787–1808.Google ScholarPubMed
Little, R. J., & Rubin, D. B. (2000). Causal effects in clinical and epidemiological studies via potential outcomes: Concepts and analytical approaches. Annual Review of Public Health, 21, 121–145.CrossRefGoogle ScholarPubMed
Lundberg, I., Johnson, R., & Stewart, B. (2021). What is your estimand? Defining the target quantity connects statistical evidence to theory. American Sociological Review, 86(3), 532–565. https://doi.org/10.31235/osf.io/ba67n.CrossRefGoogle Scholar
Pearl, J., & Bareinboim, E. (2014). External validity: From do-calculus to transportability across populations. Schweizerische Monatsschrift fur Zahnheilkunde = Revue Mensuelle Suisse D'odonto-Stomatologie/SSO, 29(4), 579–595.Google Scholar
Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal inference in statistics: A primer. John Wiley & Sons.Google Scholar
Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27–42.CrossRefGoogle Scholar
Ross, C. T., Winterhalder, B., & McElreath, R. (2021). Racial disparities in police use of deadly force against unarmed individuals persist after appropriately benchmarking shooting data on violent crime rates. Social Psychological and Personality Science, 12(3), 323–332.CrossRefGoogle Scholar