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Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data
Open accessAbstract
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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“Correlation does not imply causation (Rohrer, 2018), and no other variable significantly correlated with the perceptions of retributive justice and tax compliance intentions.”
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Toward a Theory of Lifespan Personality Trait Development
Bleidorn, W. (2024) · Annual Review of Developmental Psychology · doi:10.1146/annurev-devpsych-010923-101709 · Cited by 42
Clarke, B., Alley, L. J., Ghai, S., Flake, J. K., Rohrer, J. M., Simmons, J. P., Schiavone, S. R., & Vazire, S. (2024) · Social and Personality Psychology Compass · doi:10.1111/spc3.12979 · Cited by 33
Self-citationPauer, S., Rutjens, B. T., Brick, C., Lob, A. B., Buttlar, B., Noordewier, M. K., Schneider, I. K., & van Harreveld, F. (2024) · Social Psychological and Personality Science · doi:10.1177/19485506241263884 · Cited by 25
“Given that correlational studies may fail to detect causal processes (Rohrer, 2018), the model suggested by Siegrist and Cvetkovich (2000) might be obscured by other causal pathways involved in the interaction between trust and knowledge (e.g., Miller et al., 2016).”
Currie, C. L., & Tough, S. (2021) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-021-03591-1 · Cited by 24
Garjani, A., Hunter, R., Law, G. R., Middleton, R., Tuite-Dalton, K. A., Dobson, R., Ford, D. V., Hughes, S., Pearson, O. R., Rog, D. J., et al. (2021) · Multiple Sclerosis Journal · doi:10.1177/13524585211020435 · Cited by 25
“We chose this approach to avoid introducing bias by controlling for colliders and mediators in the regression analyses models which is a common issue in psychological research.(19) Confounding factors controlled for in each analysis have been stated in the results, and where not mentioned, adjustments for age, gender,…”
MethodHeeren, A., Hanseeuw, B. J., Cougnon, L., & Lits, G. (2021) · Psychologica Belgica · doi:10.5334/pb.1069 · Cited by 27
Vickers, A. J., Assel, M. J., Dunn, R. L., Zabor, E. C., Kattan, M. W., van Smeden, M., & Dahly, D. L. (2023) · European Urology · doi:10.1016/j.eururo.2023.04.027 · Cited by 25
Mulder, J. D., Luijken, K., de Vries, B. B. L. P., & Hamaker, E. L. (2024) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2024.2316586 · Cited by 26
“A causal DAG encodes causal assumptions about the data-generating mechanism based on domain knowledge (for an introduction on DAGs in the context of psychological science, we refer to Rohrer, 2018).”
Favero, M., Carlucci, S., Chinazzo, G., Møller, J. K., Schweiker, M., Vellei, M., & Sonta, A. J. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111903 · Cited by 22
Body Odor Disgust Sensitivity Predicts Moral Harshness Toward Moral Violations of Purity
Liuzza, M. T., Olofsson, J., Montecinos, S. C., & Lindholm, T. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.00458 · Cited by 25
“Therefore, controlling for them would have undermined our inference because we would have controlled for a mediator (Rohrer, 2018).”
BackgroundFerrar, J., Griffith, G. J., Skirrow, C., Cashdollar, N. M., Taptiklis, N., Dobson, J., Cree, F., Cormack, F. K., Barnett, J. H., & Munafo, M. R. (2021) · Journal of Medical Internet Research · doi:10.2196/26004 · Cited by 23
“If we assume that educational attainment is related to cognitive performance, then any relationship we see between the predictors of iPhone use (eg, age and health) and cognition may be distorted by collider bias [69].”
BackgroundADHD symptoms and entrepreneurial intention among Vietnamese college students: an empirical study
Duong, C. D., & Le, T. L. (2021) · Journal of Entrepreneurship in Emerging Economies · doi:10.1108/jeee-02-2021-0049 · Cited by 27
Ballou, N., & Zendle, D. I. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.107140 · Cited by 25
Pavlova, M. K., & Lühr, M. (2023) · PLoS ONE · doi:10.1371/journal.pone.0281354 · Cited by 34
“confounders in a correlational study [80].”
BackgroundA Manifesto for More Productive Psychological Games Research
Ballou, N. (2023) · Games Research and Practice · doi:10.1145/3582929 · Cited by 27
“, [49, 125]), review the basics of causal modeling [95, 122], and consider conducting more explicitly descriptive or exploratory studies (for a tutorial, see, e.”
BackgroundMoriarity, D. P., Mengelkoch, S., & Slavich, G. M. (2023) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2023.06.022 · Cited by 24
“Second, we suggest that researchers deeply consider the plausible causal structure of their variables of interest, including covariates (Rohrer, 2018; Wysocki et al., 2022).”
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Commentary: Screens, Teens, and Psychological Well-Being: Evidence From Three Time-Use-Diary Studies
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“If mediating variables are controlled for, the very processes of interest are controlled away” (Rohrer, 2018, p. 34, 37; see also Schisterman et al., 2009).”
BackgroundEcology needs a causal overhaul
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“Causal diagrams are often drawn as directed acyclic graphs (DAGs) where nodes represent variables and arrows point from cause to effect (de Mesquita, 2018; Rohrer, 2018).”
How often does random assignment fail? Estimates and recommendations
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Improving Social Science: Lessons from the Open Science Movement
Engzell, P., & Rohrer, J. M. (2020) · PS Political Science & Politics · doi:10.1017/s1049096520000967 · Cited by 23
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Binge-Watching, Self-Determination, and Well-Being
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“In the context of our analyses, it is, for example, possible that unmeasured variables (i.e., nontested mediators) confound the relation between mediator, predictor, and outcome (Fiedler et al., 2011; Kim et al., 2018; Rohrer, 2018).”
BackgroundChen, C., Li, F., Liu, C., Li, K., Yang, Q., & Ren, L. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.919692 · Cited by 25
Do We Live in a ‘Post-Truth’ Era?
Ahlstrom‐Vij, K. (2021) · Political Studies · doi:10.1177/00323217211026427 · Cited by 26
“Rohrer (2018) provides a highly accessible discussion of mediators and related causal concepts.”
Background
Metadata
- 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
- 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
- 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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