Comment: understanding Simpson’s paradox
Abstract
I thank the editor, Ronald Christensen, for the opportunity to discuss this important topic and to comment on the article by Armistead. Simpson’s paradox is often presented as a compelling demonstr...
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The appropriate way to analyze data depends crucially on what one assumes is causing what.
“In fact, Pearl shows that the appropriate way to analyze the data depends crucially on what one assumes is causing what.”
From Rethinking phylogenetic comparative methods (2018) · cites this paper as Pearl (2014)
Simpson's paradox is not really paradoxical when considered from the standpoint of Bayesian Networks.
“However, as Pearl (2014) has convincingly demonstrated, Simpson's paradox is not really paradoxical at all when considered from the standpoint of Bayesian Networks.”
From Rethinking phylogenetic comparative methods (2018) · cites this paper as Pearl (2014)
Cites
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653 · see citing works
Self-citationCausal diagrams for empirical research
PEARL, J. (1995) · Biometrika · doi:10.1093/biomet/82.4.669 · Cited by 2,355 · see citing works
Self-citationThe interpretation of interaction in contingency tables
Simpson, E. H. (1951) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1951.tb00088.x · Cited by 2,301 · cited 2× · see citing works
“Remarkably, our guesses should prove correct regardless of the parameters used in the simulation model, as long as the structure of the simulator remains the same.8 This explains how people form a consensus about which data is “more sensible” (Simpson 1951) prior to actually seeing the data.”
BackgroundMethodLimitations of the Application of Fourfold Table Analysis to Hospital Data
Berkson, J. (1946) · Biometrics Bulletin · doi:10.2307/3002000 · Cited by 1,810 · cited 2× · see citing works
“This special handling of “ colliders” reflects a general phenomenon known as Berkson’s paradox (Berkson 1946), whereby observations on a common consequence of two independent causes render those causes dependent.”
BackgroundIdentification, Inference and Sensitivity Analysis for Causal Mediation Effects
Imai, K., Keele, L., & Yamamoto, T. (2010) · Statistical Science · doi:10.1214/10-sts321 · Cited by 1,628 · see citing works
Interpretation and identification of causal mediation
Pearl, J. (2014) · Psychological Methods · doi:10.1037/a0036434 · Cited by 554 · see citing works
Self-citation[Bayesian Analysis in Expert Systems]: Comment: Graphical Models, Causality and Intervention
Pearl, J. (1993) · Statistical Science · doi:10.1214/ss/1177010894 · Cited by 258 · see citing works
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Improving the accuracy of medical diagnosis with causal machine learning
Richens, J. G., Lee, C. M., & Johri, S. (2020) · Nature Communications · doi:10.1038/s41467-020-17419-7 · Cited by 598 · see citing works
Rethinking phylogenetic comparative methods
Uyeda, J. C., Zenil-Ferguson, R., & Pennell, M. W. (2018) · Systematic Biology · doi:10.1093/sysbio/syy031 · Cited by 313 · see citing works
Love, P. E. D., Ahiaga-Dagbui, D. D., & Irani, Z. (2016) · Transportation Research Part A: Policy and Practice · doi:10.1016/j.tra.2016.08.007 · Cited by 138 · see citing works
Data Pre-Processing for Discrimination Prevention: Information-Theoretic Optimization and Analysis
Calmon, F. D. P., Wei, D., Vinzamuri, B., Ramamurthy, K. N., & Varshney, K. R. (2018) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2018.2865887 · Cited by 33 · see citing works
Differences between human and machine perception in medical diagnosis
Makino, T., Jastrzębski, S., Oleszkiewicz, W., Chacko, C., Ehrenpreis, R., Samreen, N., Chhor, C., Kim, E., Lee, J., Pysarenko, K., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-10526-z · Cited by 30 · see citing works
- Authors
- Pearl, J.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Pearl, Judea
- Venue
- The American Statistician
- Publisher
- Informa UK Limited
- Published
- 2014-01-02
- Volume
- 68
- Issue
- 1
- Pages
- 8-13
- ISSN
- 0003-1305 (ISSN-L)
- 1537-2731
- OpenAlex
- W2035841117
- Cited by
- 206
- References
- 7
- Citations by year
- 2026: 22 · 2025: 14 · 2024: 15 · 2023: 8 · 2022: 22 · 2021: 27 · 2020: 17 · 2019: 10 · 2018: 28 · 2017: 12 · 2016: 15 · 2015: 11 · 2014: 4 · 2013: 1
- FWCI
- 14.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The American Statistician (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Keywords
- Simpson's paradox, confounding, causal inference, conditional association
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