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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Stability of network models linking personality to conspiracy mentality before and during the Covid-19 pandemic
Stasielowicz, L. (2026) · Europe’s Journal of Psychology · doi:10.5964/ejop.14761 · Cited by 1
“…in the present study, which were thought to occupy a similar place in the causal chain, it cannot be ruled out that there are collider-type relationships which may bias correlation estimates (Cinelli et al., 2024; Rohrer, 2018) or that relevant varia bles were not included in the networks.”
Improved Utilization of Serial Testing Without Increased Admissions after Implementation of High-Sensitivity Troponin I: a Controlled Retrospective Cohort Study
Warren, L., Fischer, B. G., Shemesh, A. J., Scofi, J., Pandya, N., Kim, R. J., Andy, C., Rand, S., Yee, J. K., Semple, S., et al. (2023) · Journal of General Internal Medicine · doi:10.1007/s11606-023-08535-3 · Cited by 1
“23 As with many similar studies, our study has several additional limitations.”
Introduction
Heller, A., & Schmidt, P. (2024) · Routledge eBooks · doi:10.4324/9781003427469-1 · Cited by 1
Causal discovery for image datasets via feature extraction
Rawal, A., Raglin, A. J., Wang, Q., & Tang, Z. (2024) · Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications · doi:10.1117/12.3013191 · Cited by 1
Anger Expression Styles, Cynical Hostility, and the Risk for the Development of Type 2 Diabetes or Diabetes-Related Heart Complications: Secondary Analysis of the Health and Retirement Study
Ward, R., Forget, G., Lindekilde, N., Deschênes, S. S., Pouwer, F., Schmitz, N., Schram, M. T., & Burns, R. J. (2024) · Psychosomatic Medicine · doi:10.1097/psy.0000000000001344 · Cited by 1
“Variables that may mediate the associations between anger, hostility, and T2D or diabetes-related heart complications (e.g., health-related behaviors such as physical activity; physiological factors such as body mass index) were not included as covariates (37).”
A Causal View on Bias in Missing Data Imputation: The Impact of Evil Auxiliary Variables on Norming of Test Scores
Sengewald, E., Hardt, K., & Sengewald, M.-A. D.-P. (2024) · Multivariate Behavioral Research · doi:10.1080/00273171.2024.2412682 · Cited by 1
“In a DAG, the theoretical assumptions allowing for causal inference can be well displayed and different sources of bias in effect estimates can be disentangled (see e.g., Rohrer, 2018 for an easily accessible introduction to DAGs).”
Understanding linear interaction analysis with causal graphs
Kim, Y., & Jung, G. (2024) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/bmsp.12369 · Cited by 2
Can online interactions reduce loneliness in young adults during university closures in Japan? The directed acyclic graphs approach
Kambara, K., Toya, A., Lee, S., Shimizu, H., Abe, K., Shigematsu, J., Zhang, Q., Abe, N., Hayase, R., Abe, N., et al. (2024) · Asian Journal Of Social Psychology · doi:10.1111/ajsp.12658 · Cited by 1
How large must an associational mean difference be to support a causal effect?
Höfler, M., Pronizius, E., & Buchanan, E. M. (2024) · Methodology · doi:10.5964/meth.14579 · Cited by 1
Psychological risk and protective factors of immunity: a cross-cultural network analysis
Chalmers, R., Cervin, M., Choo, C. C., Sutton, A., Barcaccia, B., Couyoumdjian, A., Pallini, S., Iqbal, N., Reid, V. M., Singh, N. N., et al. (2024) · Current Psychology · doi:10.1007/s12144-024-07178-9 · Cited by 1
“DAGs consist of nodes as per GGMs and arrows depicting directed edges which represent probabilities of causal pathways that are non-circular (Rohrer, 2018).”
A scoping review and synthesis of the literature on the relation between mobility impairment and cognitive impairment
Rule, P. D., Trammell, V. L., Hill, P. L., Pearlstein, J. G., & Willroth, E. C. (2025) · Disability and Rehabilitation · doi:10.1080/09638288.2024.2449392 · Cited by 1
“…a measure of mobility function, it is unclear whether this should be considered a confounder of the relation between mobility function and cognitive function. including covariates that are not true confounders can lead to overadjustment, decreased power, or misinterpretation of the results [72].”
Investigating the Relation Between Second Language Proficiency and Study Success Using a Causal Inference Approach
Spit, S., Andringa, S. J., & Ryan, O. (2025) · Language Learning · doi:10.1111/lang.12713 · Cited by 1
“The average marginal NDEs (Norton et al., 2019; Rohrer et al., 2021) are most informative in this respect and indicate that the causal effect in B1 examinees who pursued an MBO1/2 program is negative, − 5.9% points, whereas there is no indication of an effect in B1 examinees who pursued an MBO3/4…”
Influential
Triggers of curiosity in social constructivist classroom discourse
Ali, F., Wang, Y., Wang, S. J.-W., & Zhu, G.-X. (2025) · npj Science of Learning · doi:10.1038/s41539-025-00330-5 · Cited by 3
Quantifying hydrogen technology acceptance: Insights from Bayesian networks
Herr, D. Z., Scovell, M., Kinaev, N. N., & Vaisman, R. (2025) · Energy and Climate Change · doi:10.1016/j.egycc.2025.100201 · Cited by 1
Evolution of the Classical Leadership Styles in a Digital Environment
Lindov, I. (2025) · Proceedings of the ... International Conference on Business Excellence · doi:10.2478/picbe-2025-0062 · Cited by 1
“As it only shows association (Rohrer, 2018), but does not imply causation, regression analysis is further employed to suggest causation, if certain assumptions are met.”
Personality Predictors of Health, Well-Being, Relationships, and Employment in Namibia, Kenya, and South Africa: A Three-Year Longitudinal Study
Haehner, P., Shirima, C. M., Kura, M., Naudé, L., Hopwood, C. J., & Thalmayer, A. G. (2026) · European Journal of Personality · doi:10.1177/08902070261477867 · Cited by 1
A Model of Causal Explanation on Neural Networks for Tabular Data
Isozaki, T., Yamamoto, M., & Noda, A. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.07.119 · Cited by 1
“The journey of understanding various phenomena in natural sciences such as epidemiology and in social sciences such as psychology is evidence that humans try to understand things through causal relationships (e.g., [8], [9]).”
Education research: Getting started
Terry, J., Le, L. J., Jones, R., Sergeant, J. C., Dunham, B., & Jones, E. M. (2025) · Teaching Statistics · doi:10.1111/test.70008 · Cited by 1
Investigating the trivariate reciprocal longitudinal relationships between purpose, loneliness, and cognitive function
Pfund, G. N., Ong, A. D., Mroczek, D. K., James, B. D., Jackson, K., Rose, D., Barnes, L. L., Bennett, D. A., & Graham, E. K. (2025) · The Journals of Gerontology Series B · doi:10.1093/geronb/gbaf161 · Cited by 2
Enhancing social science research on cyberbullying through human machine collaboration
Ramos, A. B., Reneses, M., Pérez, J., Valverde, G., Awad, E., López, G., & Castro, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-16149-4 · Cited by 1
Individual differences in self-esteem trajectories after negative life events: The role of the Big Five personality traits and perceived event characteristics
Schellenberg, L., Altorfer, M., & Haehner, P. (2025) · European Journal of Personality · doi:10.1177/08902070251383960 · Cited by 1
“Additionally, neuroticism has been linked to less positive self-esteem trajectories, with effects observed across time spans from several months to multiple years (e.g., Fetvadjiev & He, 2019; Poorthuis et al., 2014; Wagner, Lüdtke et al., 2013; Weidmann et al., 2017, 2018).”
School Refusal and its Underlying Mechanisms: A Network Perspective on Individual and Environmental Determinants
Seçer, İ., Çi̇men, F., Ulaş, S., Ay, E., Tatlı, E., Garcia, D., & Sunay, C. (2025) · School Mental Health · doi:10.1007/s12310-025-09804-8 · Cited by 2
“DAG is a Bayesian network method that provides information about the connections between nodes and potential causal pathways (Jones et al., 2018; Rohrer, 2018).”
Building causal models in pain research: the case of executive functioning and transitions in pain states
Paepe, A. L. D., Gibby, A., Lisboa, L. O., Ehrhardt, B., Nunes, M. A., Fisher, E., Keogh, E., Eccleston, C., Woolley, C. S. C., McBeth, J., et al. (2025) · Pain · doi:10.1097/j.pain.0000000000003833 · Cited by 1
“11,40,45,50 A DAG thereby prompts researchers to engage in a constructive discussion about causal relationships.”
Rising Income Inequality and Longer Work Hours: Global Trends and Longitudinal Evidence From the United States and China
Liu, W., Sommet, N., & Du, H. (2025) · Social Psychological and Personality Science · doi:10.1177/19485506251388682 · Cited by 1
“Because changes in GDP per capita may affect both changes in income inequality (Topuz, 2022) and work hours (Schein & Haruvi, 2017), GDP per capita may act as a confounder (Rohrer, 2018) and should be included as a control variable.”
The price of fame? Mortality risk among famous singers
Hepp, J., Heine, C., Schliebener, M., & Dufner, M. (2025) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2025-224589 · Cited by 1
“25 Accordingly, we employed a matched-pair design 26 to examine whether fame adds an additional factor of mortality risk within an already vulnerable group.”
Within‐person association between affect and physical activity related to depressive symptomatology: A systematic review and methodological reflections
Vuuregge, A. H., Zhang, Y., Meulmeester, W. M., Bockting, C., Lucassen, P. J., Heininga, V. E., Schrantee, A., & Mul, J. D. (2025) · Applied Psychology Health and Well-Being · doi:10.1111/aphw.70086 · Cited by 2
“It is known that improper statistical control can induce biased results (Rohrer, 2018; Ryan et al., 2022) and that the different choices of time-lags when aggregating passive measures data can lead to different conclusions (Langener, Stulp, et al., 2024).”
The mediating role of recreational physical activity and dietary behavior in the relationship between family affluence and mental well-being: an interventional effects approach
Loeys, T., Poppe, L., Dierckens, M., & Moerkerke, B. (2025) · Journal of Behavioral Medicine · doi:10.1007/s10865-025-00615-5 · Cited by 1
“There are two types of variables that researchers should avoid controlling for without caution (Rohrer, 2018 ): colliders and mediators.”

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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