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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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Smartphones within Psychological Science
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408 · Cited by 14
Erceg, N., Galić, Z., & Bubić, A. (2019) · Europe’s Journal of Psychology · doi:10.5964/ejop.v15i1.1696 · Cited by 15
“In cases like this, it is possible that controlling for a collider (whether statistically or by sampling) could distort and bias the relationship between the predictor and outcome (see Rohrer, 2018).”
BackgroundRock the MIC: The Matrix of Implied Causation, a Tool for Experimental Design and Model Checking
Brick, T. R., & Bailey, D. H. (2020) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245920922775 · Cited by 15
“…example, structural equation models assuming the three causal structures discussed in Example 1 later in this article—the chain X → Y → Z, the chain X ← Y ← Z, and the fork X ← Y → Z (for an introduction to graphical causal models, see Rohrer, 2018)—will fit identically for any covariance matrix.”
BackgroundUsing Mobile Sensors to Study Personality Dynamics
Wiernik, B. M., Öneş, D. S., Marlin, B. M., Giordano, C., Dilchert, S., Mercado, B. K., Stanek, K. C., Birkland, A. S., Wang, Y., Ellis, B. D., et al. (2020) · European Journal of Psychological Assessment · doi:10.1027/1015-5759/a000576 · Cited by 18
Evolution of Quantitative Research Methods in Strategic Management
Shaver, J. M. (2021) · doi:10.1093/oso/9780190090883.003.0005 · Cited by 15
A workflow for causal inference in cross-cultural psychology
Bulbulia, J. A. (2022) · Religion Brain & Behavior · doi:10.1080/2153599x.2022.2070245 · Cited by 15
“For this reason, reporting “predictions” in the absence of a causal framework is likely to mislead (Bulbulia et al., 2021; Hernán, 2018; McElreath, 2020; Pearl, 2009; Rohrer, 2018; VanderWeele, 2015; Westreich & Greenland, 2013).”
BackgroundNo meta-analytical effect of economic inequality on well-being or mental health
Sommet, N., Fillon, A. A., Rudmann, O., Cunha, A. R. S., & Ehsan, A. (2025) · Nature · doi:10.1038/s41586-025-09797-z · Cited by 19
“The DAG 105 that informed these decisions a priori is presented in Extended Data Fig.”
InfluentialPhysiological stress reactivity and recovery: Some laboratory results transfer to daily life
Bamert, M., & Inauen, J. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.943065 · Cited by 14
“Deciding which factors to control for is complicated and should be well-justified, because controlling for colliding variables and mediators could shift the value of the causal impact of interest away from the estimation of a relationship and lead to bias (Rohrer, 2018).”
BackgroundMethodCausal reasoning with causal graphs in educational technology research
Weidlich, J., Hicks, B., & Drachsler, H. (2023) · Educational Technology Research and Development · doi:10.1007/s11423-023-10241-0 · Cited by 14
“Most prominently employed in epidemiology (Munafo et al., 2018), there are numerous recent efforts to introduce and apply these principles to psychology (Lee 2012; Rohrer 2018), sociology (Elwert & Winship, 2014), as well as many other fields (Griffiths et al., 2020).”
Long-term causal effects of far-right terrorism in New Zealand
Bulbulia, J. A., Afzali, M. U., Yogeeswaran, K., & Sibley, C. G. (2023) · PNAS Nexus · doi:10.1093/pnasnexus/pgad242 · Cited by 15
“Thus, although in observational settings the counterfactual contrast required for causal inference, , many psychological scientists operate under the assumption of an equivalence (16, 39–41).”
Mindful Self-Compassion as an Antidote to Burnout for Mental Health Practitioners
Lyon, T. R., & Galbraith, A. (2023) · Healthcare · doi:10.3390/healthcare11202715 · Cited by 20
“If an alternative explanation exists, due to unaccounted for variables or confounding covariates, this could limit internal validity [82] or result in a biased estimate of the effect size [83].”
BackgroundThe relations among prosocial behavior, hedonic, and eudaimonic well‐being in everyday life
Gregori, F., López‐Pérez, B., Manfredi, L., Eisenberg, N., Lundie, D. C., Lee, S., Gerbino, M., Pastorelli, C., & Zuffianò, A. (2024) · Journal of Personality · doi:10.1111/jopy.12937 · Cited by 14
Schleifenbaum, L., Stern, J., Driebe, J. C., Wieczorek, L. L., Gerlach, T. M., Arslan, R. C., & Penke, L. (2022) · Hormones and Behavior · doi:10.1016/j.yhbeh.2022.105202 · Cited by 14
“…the effect of PBFW), mediator (when fertile, women increase contact and this increased contact leads to increased male ratings) or collider (Rohrer, 2018) variable (when fertile, women increase contact to their partners and men's perceptions of women's sexual desire also lead to increased…”
BackgroundStasielowicz, L. (2022) · Scientific Reports · doi:10.1038/s41598-022-15769-4 · Cited by 15
Research Design and Issues of Validity
Brewer, M. B., & Crano, W. D. (2024) · Cambridge University Press eBooks · doi:10.1017/9781009170123.007 · Cited by 15
Heeren, A., Hanseeuw, B. J., Cougnon, L., & Lits, G. (2021) · doi:10.31234/osf.io/b34aj · Cited by 14
Revisiting financial inclusion-stability nexus: cross-country heterogeneity
Saha, M., & Dutta, K. D. (2022) · Journal of Financial Economic Policy · doi:10.1108/jfep-06-2021-0162 · Cited by 13
Oh, J., Purol, M. F., Weidmann, R., Chopik, W. J., Kim, E. S., Baranski, E. N., Schwaba, T., Lodi‐Smith, J., & Whitbourne, S. K. (2022) · Journal of Research in Personality · doi:10.1016/j.jrp.2022.104237 · Cited by 14
Attachment and family functioning across three generations
Vitulić, H. S., Gosar, D., & Prosen, S. (2022) · Family Process · doi:10.1111/famp.12787 · Cited by 13
Predictive Analysis of Airport Safety Performance: Case Study of Split Airport
Bartulović, D., & Steiner, S. (2023) · Aerospace · doi:10.3390/aerospace10030303 · Cited by 13
Alvarez-Vargas, D., Braithwaite, D. W., Lortie‐Forgues, H., Moore, M. M., Castro, M., Wan, S., Martin, E. A., & Bailey, D. H. (2023) · PLoS ONE · doi:10.1371/journal.pone.0286403 · Cited by 13
Critical issues in statistical causal inference for observational physics education research
Adlakha, V., & Kuo, E. (2023) · Physical Review Physics Education Research · doi:10.1103/physrevphyseducres.19.020160 · Cited by 13
Abplanalp, S. J., BRAFF, D. L., Light, G. A., Joshi, Y. B., Nuechterlein, K. H., & Green, M. F. (2024) · Psychological Medicine · doi:10.1017/s0033291724000023 · Cited by 14
“Cross-sectional DAGs can illustrate various dependent structures among nodes and provide insights beyond GGMs (Grosz, Rohrer, & Thoemmes, 2020; Rohrer, 2018; Wysocki, Lawson, & Rhemtulla, 2022).”
BackgroundPrike, T., Holloway, J., & Ecker, U. K. H. (2024) · advances in/psychology · doi:10.56296/aip00025 · Cited by 14
Public acceptance of civilian drones and air taxis in Germany: A comprehensive overview
End, A., Barzantny, C., Stolz, M., Grupe, P., Schmidt, R., Papenfuß, A., & Eißfeldt, H. (2025) · CEAS Aeronautical Journal · doi:10.1007/s13272-025-00834-1 · Cited by 13
“Instead, they could merely indicate covariations of variables or even influences in the opposite direction, i.e., influences of drone-related attitudes on the considered variables [55].”
Coenen, L. (2022) · European Journal of Communication · doi:10.1177/02673231221082244 · Cited by 17
“to always report Directed Acyclic Graphs together with the sizes of the respective relationships might certainly help in this regard (Pearl, 2009; Rohrer, 2018).”
BackgroundTemperamental and Theoretical Contributions to Clinical Psychology
Kagan, J. (2022) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-071720-014404 · Cited by 15
Dixon, P., Harrison, S., Hollingworth, W., Davies, N. M., & Smith, G. D. (2022) · Economics & Human Biology · doi:10.1016/j.ehb.2022.101154 · Cited by 13
The long-term benefits of Montessori pre-K for Latinx children from low-income families
Ansari, A., & Winsler, A. (2020) · Applied Developmental Science · doi:10.1080/10888691.2020.1781632 · Cited by 19
“That is, propensity scores serve the same purpose as covariate adjustment because they do not change the causal identification strategy (Rohrer, 2018).”
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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