Cited by 1,002Advanced Causal Inference TechniquesBayesian Modeling and Causal InferencePhilosophy of Biology and Scientific Explanation

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Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data

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Rohrer, Julia M.·2018·Advances in Methods and Practices in Psychological Science

DOI 10.1177/2515245917745629

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Abstract

Correlation does not imply causation; but often, observational data are the only option, even though the research question at hand involves causality. This article discusses causal inference based on observational data, introducing readers to graphical causal models that can provide a powerful tool for thinking more clearly about the interrelations between variables. Topics covered include the rationale behind the statistical control of third variables, common procedures for statistical control, and what can go wrong during their implementation. Certain types of third variables—colliders and mediators—should not be controlled for because that can actually move the estimate of an association away from the value of the causal effect of interest. More subtle variations of such harmful control include using unrepresentative samples, which can undermine the validity of causal conclusions, and statistically controlling for mediators. Drawing valid causal inferences on the basis of observational data is not a mechanistic procedure but rather always depends on assumptions that require domain knowledge and that can be more or less plausible. However, this caveat holds not only for research based on observational data, but for all empirical research endeavors.

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“Directed Acyclical Graphs (DAGs) were designed to represent causal assumptions irrespective of the speci ﬁ c empirical approach [43] – [46] and have already been received as useful in technology implementation studies in science education [47], [48].”
Spatial parameters of eye-movement control during reading in Russian-speaking children
Lopukhina, A., Eremicheva, T., Dragoy, O., & Parshina, O. (2026) · Journal of Experimental Child Psychology · doi:10.1016/j.jecp.2026.106564
Modeling frameworks in nutritional epidemiology matter: comparing isotemporal and time-lagged Bayesian and frequentist approaches of carbohydrate intake and adiposity
Titensor, S., Ebbert, J. L., Camacho, D., Corte, K. W. D., Palmeira, A. L., Stubbs, R. J., Horgan, G. W., Heitmann, B. L., & Corte, D. D. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1700898
“Posterior distributions were estimated via Markov Chain Monte Carlo (MCMC) sampling using the ulam function from the rethinking package (12), which interfaces with the Stan probabilistic programming language (20, 24, 25, 36, 37).”
Additional file 5 of Land use impacts on parasitic infection: a cross-sectional epidemiological study on the role of irrigated agriculture in schistosome infection in a dammed landscape
Lund, A. J., Rehkopf, D. H., Sokolow, S. H., Sam, M. M., Jouanard, N., Schacht, A., Senghor, S., Fall, A., Riveau, G. J., Leo, G. A. D., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14256935
Additional file 3 of Land use impacts on parasitic infection: a cross-sectional epidemiological study on the role of irrigated agriculture in schistosome infection in a dammed landscape
Lund, A. J., Rehkopf, D. H., Sokolow, S. H., Sam, M. M., Jouanard, N., Schacht, A., Senghor, S., Fall, A., Riveau, G. J., Leo, G. A. D., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14256929
Additional file 10 of Land use impacts on parasitic infection: a cross-sectional epidemiological study on the role of irrigated agriculture in schistosome infection in a dammed landscape
Lund, A. J., Rehkopf, D. H., Sokolow, S. H., Sam, M. M., Jouanard, N., Schacht, A., Senghor, S., Fall, A., Riveau, G. J., Leo, G. A. D., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14256911
Additional file 1 of Housing conditions and risk of incident COPD: a Danish cohort study, 2000–2018
Kloster, S., Kirkegaard, A. M., Davidsen, M., Christensen, A. I., Nielsen, N. S., Gunnarsen, L., Vestbo, J., & Ersbøll, A. K. (2024) · Figshare · doi:10.6084/m9.figshare.26122633
Additional file 2 of Land use impacts on parasitic infection: a cross-sectional epidemiological study on the role of irrigated agriculture in schistosome infection in a dammed landscape
Lund, A. J., Rehkopf, D. H., Sokolow, S. H., Sam, M. M., Jouanard, N., Schacht, A., Senghor, S., Fall, A., Riveau, G. J., Leo, G. A. D., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14256923
Additional file 7 of Land use impacts on parasitic infection: a cross-sectional epidemiological study on the role of irrigated agriculture in schistosome infection in a dammed landscape
Lund, A. J., Rehkopf, D. H., Sokolow, S. H., Sam, M. M., Jouanard, N., Schacht, A., Senghor, S., Fall, A., Riveau, G. J., Leo, G. A. D., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14256941
Additional file 2 of Early sexual initiation and risky sexual practices among alcohol- and tobacco-using young adults in Taiwan: mediation analysis of preceding-sex use of illicit drugs
Wu, T. L., Ting, T.-T., Chen, C.-Y., Su, L.-W., & Chen, W. J. (2020) · Figshare · doi:10.6084/m9.figshare.13185783
Additional file 3 of The psychosocial situation of families caring for children with rare diseases during the COVID-19 pandemic: results of a cross-sectional online survey
Rihm, L., Dreier, M., Rezvani, F., Wiegand‐Grefe, S., & Dirmaier, J. (2024) · Figshare · doi:10.6084/m9.figshare.26557580
Additional file 8 of Land use impacts on parasitic infection: a cross-sectional epidemiological study on the role of irrigated agriculture in schistosome infection in a dammed landscape
Lund, A. J., Rehkopf, D. H., Sokolow, S. H., Sam, M. M., Jouanard, N., Schacht, A., Senghor, S., Fall, A., Riveau, G. J., Leo, G. A. D., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14256944
Additional file 6 of Land use impacts on parasitic infection: a cross-sectional epidemiological study on the role of irrigated agriculture in schistosome infection in a dammed landscape
Lund, A. J., Rehkopf, D. H., Sokolow, S. H., Sam, M. M., Jouanard, N., Schacht, A., Senghor, S., Fall, A., Riveau, G. J., Leo, G. A. D., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14256938
Systematic, quality appraisal of household recycling influences research finds evidence is mostly insufficientPreprint
Macklin, J., Smith, L. D. G., & Curtis, J. M. (2023) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/kyvea
Additional file 1 of The psychosocial situation of families caring for children with rare diseases during the COVID-19 pandemic: results of a cross-sectional online survey
Rihm, L., Dreier, M., Rezvani, F., Wiegand‐Grefe, S., & Dirmaier, J. (2024) · Figshare · doi:10.6084/m9.figshare.26557574
Additional file 1 of Land use impacts on parasitic infection: a cross-sectional epidemiological study on the role of irrigated agriculture in schistosome infection in a dammed landscape
Lund, A. J., Rehkopf, D. H., Sokolow, S. H., Sam, M. M., Jouanard, N., Schacht, A., Senghor, S., Fall, A., Riveau, G. J., Leo, G. A. D., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14256917
Additional file 1 of Early sexual initiation and risky sexual practices among alcohol- and tobacco-using young adults in Taiwan: mediation analysis of preceding-sex use of illicit drugs
Wu, T. L., Ting, T.-T., Chen, C.-Y., Su, L.-W., & Chen, W. J. (2020) · Figshare · doi:10.6084/m9.figshare.13185780
Additional file 2 of The psychosocial situation of families caring for children with rare diseases during the COVID-19 pandemic: results of a cross-sectional online survey
Rihm, L., Dreier, M., Rezvani, F., Wiegand‐Grefe, S., & Dirmaier, J. (2024) · Figshare · doi:10.6084/m9.figshare.26557577

Authors: Rohrer, J. M.
Affiliations: German Institute for Economic Research (DE)
Corresponding author: Rohrer, Julia M.
ORCID: Rohrer, Julia M.
Venue: Advances in Methods and Practices in Psychological Science
Publisher: SAGE Publications
Published: 2018-01-29
Volume: 1
Issue: 1
Pages: 27-42
ISSN: 2515-2459 (ISSN-L)
2515-2467
DOI: 10.1177/2515245917745629
OpenAlex: W2776946813
Cited by: 1,002
References: 47
Citations by year: 2026: 124 · 2025: 145 · 2024: 150 · 2023: 147 · 2022: 122 · 2021: 132 · 2020: 137 · 2019: 31 · 2018: 14
FWCI: 65.35
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Advances in Methods and Practices in Psychological Science (publishedVersion)
MPG.PuRe (Max Planck Society) (submittedVersion)
OSF Preprints (OSF Preprints) (submittedVersion, cc-by)
OSF Preprints (OSF Preprints) (submittedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
Keywords: causal inference, observational data, causal graphical models, confounding, third variable, particle colliders, mediation, statistical control, causal assumptions, correlation versus causation

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Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data

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