The DOI matches its Crossref record; 46 of the 47 references it cites were found in a registry or an index.
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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Cites
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MethodWhy Propensity Scores Should Not Be Used for Matching
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Cited by
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Network analysis of multivariate data in psychological science
Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00055-w · Cited by 1,371
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Collider bias undermines our understanding of COVID-19 disease risk and severity
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A Crash Course in Good and Bad Controls
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“…fi elds, such as sociology (Elwert, 2013; Morgan and Winship, 2015), economics (Hünermund and Bareinboim, 2019; Cunningham, 2021), psychology (Rohrer, 2018), epidemiology (Greenland, Pearl and Robins, 1999; Hernán and Robins, 2020) and statistics (Pearl et al., 2009; Pearl, Glymour and…”
performance: Assessment of Regression Models Performance
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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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