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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Background
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Williams, J. A., Russ, D., Bravo-Merodio, L., Cardoso, V. R., Pendleton, S. C., Aziz, F., Acharjee, A., & Gkoutos, G. V. (2021) · Genes · doi:10.3390/genes12071029 · Cited by 5
“Unlike confounders, conditioning on colliders in regression introduces bias into the association between exposure and outcome [34].”
Background
Resources in women's social networks for food shopping are more strongly associated with better dietary quality than people: A cross-sectional study
Shand, C., Crozier, S., Vassilev, I., Penn-Newman, D., Dhuria, P., Cooper, C. C., Rogers, A. E., Baird, J., & Vogel, C. (2021) · Social Science & Medicine · doi:10.1016/j.socscimed.2021.114228 · Cited by 10
Trait Anger, Hostility, and the Risk of Type 2 Diabetes and Diabetes- Related Complications: A Systematic Review of Longitudinal Studies
Deschênes, S. S., Mohseni, M., Lindekilde, N., Forget, G., Burns, R. J., Pouwer, F., & Schmitz, N. (2022) · Current Diabetes Reviews · doi:10.2174/1573399818666220329185229 · Cited by 7
The effects of spanking on psychosocial outcomes: revisiting genetic and environmental covariation
Barbaro, N., Connolly, E. J., Sogge, M., Shackelford, T. K., & Boutwell, B. B. (2022) · Journal of Experimental Criminology · doi:10.1007/s11292-021-09496-5 · Cited by 6
“All univariate and bivariate models were estimated using M plus 8.1 (Muthén & Muthén, 1998–2018) with full information maximum likelihood estimation and using the WLMSV estimator.”
BackgroundMethodInfluential
Eye movements reveal that low confidence precedes deliberation
Purcell, Z. A., Wastell, C. A., & Sweller, N. (2022) · Quarterly Journal of Experimental Psychology · doi:10.1177/17470218221126505 · Cited by 6
“We Endogeneity and the Confidence-Deliberation Mechanism Endogeneity occurs when a predictor (X) may be affected by an outcome (Y) or both may be influenced by a third, usually unknown or unmeasured, variable (see Antonakis et al., 2010; Rohrer, 2018).”
Background
Deep Clinical Phenotyping of Schizophrenia Spectrum Disorders Using Data-Driven Methods: Marching towards Precision Psychiatry
Habtewold, T. D., Hao, J., Liemburg, E. J., Baştürk, N., Bruggeman, R., & Alizadeh, B. Z. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060954 · Cited by 6
“Fourth, as efforts have been devoted to unravelling the pathophysiology of SSD, it can also be beneficial to further examine the interrelatedness and distinctiveness of positive and negative symptoms, cognitive impairments and psychosocial functions [42] using causal discovery models [53,54].”
Background
Changing behavior while having climate change in mind? An investigation of social-psychological predictors for specific pro-environmental behaviors in the energy domain
Brohmer, H., Köstenbaumer, D., Thaller, A., Fleiß, E., Athenstaedt, U., & Posch, A. (2023) · Energy Efficiency · doi:10.1007/s12053-023-10157-x · Cited by 7
“Moreover, the size and direction of the effects of additional variables, along with potential confounding factors, should be carefully considered beforehand (see Rohrer, 2018).”
Background
Discovering causality for efficient cooperation in multi-agent environments
Pina, R., Silva, D., & Artaud, C. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130358 · Cited by 9
Negligible effects of birth order on selection into scientific and artistic careers, creativity, and status attainment
Damian, R. I., & Spengler, M. (2020) · European Journal of Personality · doi:10.1177/0890207020969010 · Cited by 7
“A confound is a variable that is theorized to have a causal effect on both the predictor and the outcome of interest (Rohrer, 2018)—in our case, a confound should have a theorized causal effect on both birth order and career and status attainment outcomes.”
Background
Temperament and Brain Networks of Attention
Rothbart, M. K., Posner, M. I., & Sheese, B. E. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108264822.016 · Cited by 8
Accuracy in Person Perception
Biesanz, J. C., & Wallace, J. D. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108264822.006 · Cited by 7
The Einstein effect: Global evidence for scientific source credibility effects and the influence of religiosityPreprint
Hoogeveen, S., Haaf, J. M., Bulbulia, J. A., Ross, R. M., McKay, R., Altay, S., Bendixen, T., Berniûnas, R., Cheshin, A., Gentili, C., et al. (2020) · doi:10.31234/osf.io/sf8ez · Cited by 6
Quantifying Causal Pathways of Teleconnections
Kretschmer, M. (2022) · EGU General Assembly Conference Abstracts · doi:10.5194/egusphere-egu22-8246 · Cited by 9
Individual Differences in Changes in Subjective Well-Being: The Role of Event Characteristics After Negative Life EventsPreprint
Haehner, P., Kritzler, S., & Luhmann, M. (2023) · doi:10.31234/osf.io/3mjr7 · Cited by 6
Compassion for All: Real-World Online Donations Contradict Compassion Fade
Meier, D. S. (2024) · Nonprofit and Voluntary Sector Quarterly · doi:10.1177/08997640241255707 · Cited by 5
“The directed acyclic graph (DAG; Rohrer, 2018) that visualizes this assumption is shown in Figure 2.”
Causal Inference for Dummies: A Tutorial on Directed Acyclic Graphs and Balancing Weights
Primbs, M. A., Bijlstra, G., Holland, R. W., & Thoemmes, F. J. (2025) · Social Cognition · doi:10.1521/soco.2025.43.3.217 · Cited by 6
Models as Prediction Machines: How to Convert Confusing Coefficients Into Clear Quantities
Rohrer, J. M., & Arel‐Bundock, V. (2026) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459261424825 · Cited by 6
“Determining whether a causal interpretation is warranted requires careful consideration of identification assumptions (for an introduction in psychology, see Rohrer, 2018).”
InfluentialSelf-citation
The Impact of Eco-Anxiety and Extreme Weather Proximity on Young People’s Happiness and Life Satisfaction: A Natural Experiment
Fernandez-Urbano, R., Bulut, H., & Samuel, R. (2025) · Journal of Happiness Studies · doi:10.1007/s10902-025-00950-z · Cited by 6
“…above is widely applied in the economics of happiness research–particularly in studies focusing on youth populations–the inclusion of certain control variables that do not necessarily precede eco-anxiety (i.e., intervening variables or colliders) can introduce overcontrol bias (Rohrer, 2018 ).”
Optimization and Performance Evaluation of Electrochemical Treatment of Tannery Wastewater Using Mixed Metal Oxide Electrode in Batch and Once-Through Continuous Operation
Meena, S. K., Meena, S. O., Sangal, V. K., & Dehariya, N. K. (2025) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ae1f3d · Cited by 5
A Systematic Review and Taxonomy of Neuroticism Interventions for the General Public
Wright, A. J., Haehner, P., Hopwood, C. J., & Bleidorn, W. (2026) · Personality Science · doi:10.1177/27000710261421531 · Cited by 7
Modeling personality antecedents and second language self-efficacy constructs with emerging adults in Japan: Domain-specific matching for assessing global competence in applied contexts
Kabir, R. S., Kramer, B., Koike, M., & Sponseller, A. C. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1032573 · Cited by 7
“…of communication in Harris, 2022; related self-concept measures, Leeming and Harris, 2022), or consideration of detailed models with mediators or covariate adjustments for related relationships or pathways (e.g., SES), beginning with graphical causal models for observational data (Rohrer, 2018).”
Background
Narcissistic Admiration and Narcissistic Rivalry: Associations With Accuracy and Bias in Perceptions of Romantic Partners
Seidman, G., Zeigler‐Hill, V., & Iida, M. (2023) · Journal of Social and Personal Relationships · doi:10.1177/02654075231161165 · Cited by 5
“…rather than probability sampling because it is possible that the likelihood of individuals selecting to participate in the study may have been impacted by their possession of certain characteristics which could have altered the associations observed in the present studies (see Rohrer, 2018).”
Result
Asynchronous student engagement in analysis of climate data achieves learning objectives related to climate change understanding, statistical competence, and climate anxiety
Meixner, T., Ciancarelli, B., Farrell, E. P., García, D. S., Josek, T., Kelly, M., Meister, P. A., Soule, D. C., & Darner, R. (2023) · Journal of Geoscience Education · doi:10.1080/10899995.2023.2193810 · Cited by 7
“Causation can be claimed when we have deep, collective knowledge to identify the mechanism causing the observed correlation (Rohrer, 2018).”
Father trait anger and associations with father–infant bonding and caregiving: The mediating role of mentalizing
Francis, L. M., Greenwood, C. J., Enticott, P. G., Mansour, K. A., Smith, I., Graeme, L. G., Olsson, C. A., Milgrom, J., Skouteris, H., & Macdonald, J. A. (2023) · Family Process · doi:10.1111/famp.12912 · Cited by 5
Technology use and integration in state-funded primary schools: a structural equation model
Ventista, O. M., Tsani, P., Brown, C., Coe, R., & Polydoros, G. (2024) · Education 3-13 · doi:10.1080/03004279.2024.2351013 · Cited by 5
“According to Rohrer (2018), non-response bias exists when the variable of interest is related to the questionnaire completion.”
Understanding, fast and shallow: Individual differences in memory performance associated with cognitive load predict the illusion of explanatory depth
Martínez, C. D. G., & Corredor, J. (2024) · Memory & Cognition · doi:10.3758/s13421-024-01616-6 · Cited by 5
Contributions of Age-Related and Audibility-Related Deficits to Aided Consonant Identification in Presbycusis: A Causal-Inference Analysis
Varnet, L., Léger, A. C., Boucher, S., Bonnet, C., Petit, C., & Lorenzi, C. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.640522 · Cited by 5
“causal effect estimates on the basis of observational data (Rohrer, 2018).”
Background
Reconsidering reconsent: Threats to internal and external validity when participants reconsent after debriefing
Hodson, G. (2022) · British Journal of Psychology · doi:10.1111/bjop.12561 · Cited by 6
Why twin studies are important for health span science research: the case of maltreatment of aging adults
Boutwell, B. B., Narvey, C. S., Helton, J. J., & Piquero, A. R. (2022) · BMC Geriatrics · doi:10.1186/s12877-022-03440-6 · Cited by 4
“If we assume that knowledge of a particular topic will generally always be less than complete, we confront the reality that statistical control can often prove inferior to experimental control [10, 24, 26].”
BackgroundInfluential
Visual Argument Structure Tool (VAST) Version 1.0
Leising, D., Grenke, O., & Cramer, M. (2023) · Meta-Psychology · doi:10.15626/mp.2021.2911 · Cited by 5
“Note that this diverges from recommendations in the literature on Directed Acyclic Graphs (Rohrer, 2018).”
BackgroundMethodInfluential
Correlations among Fatigue Indicators, Subjective Perception of Fatigue, and Workload Settings in Flight Operations
Bartulović, D., Steiner, S., Fakleš, D., & Jeličić, M. (2023) · Aerospace · doi:10.3390/aerospace10100856 · Cited by 5
“…between cause and effect [58], the role that human factors play in major aviation accidents [59], the use of causal models to control and manage aircraft accident risk [60], graphical causal models that can serve as powerful tools for detecting interrelations between variables [61], and others.”
Background

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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