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Randomization, Statistics, and Causal Inference
Abstract
This paper reviews the role of statistics in causal inference. Special attention is given to the need for randomization to justify causal inferences from conventional statistics, and the need for random sampling to justify descriptive inferences. In most epidemiologic studies, randomization and random sampling play little or no role in the assembly of study cohorts. I therefore conclude that probabilistic interpretations of conventional statistics are rarely justified, and that such interpretations may encourage misinterpretation of nonrandomized studies. Possible remedies for this problem include deemphasizing inferential statistics in favor of data descriptors, and adopting statistical techniques based on more realistic probability models than those in common use.
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“The latter do not take into account other uncertainties that arise from a study's design, implementation, and methods of measurement [ 209 ].”
From Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration (2007) · cites this paper as [209]
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“…randomization” to non-randomized observational data (Rubin 1974) to allow hypothesis testing (Fisher 1971), others have cautioned against such an approach citing inherent sampling biases and Type I errors in significance tests applied to observational datasets (Greenland 1990; Copas and Li 1997).”
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“Randomized controlled trials (RCTs) have uniquely superior elements (e.g., Greenland, 1990), but their methodological requirements often result in studies that bear little resemblance to the circumstances of agency-based practice in human service settings.”
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Andersson, E. A. (2026) · Informatics · doi:10.3390/informatics13080136
“It also probabilistically excludes most confounding conditions, such as unmeasured differences in intelligence or socioeconomic status [30,31].”
Coté, L. E., Zamora, A. N., Salcido, J. J., Doren, S. V., Manocha, A., Munoz, G. O., Law, E. W., & Baranger, A. M. (2026) · Laboratories · doi:10.3390/laboratories3030016
“…whether there were statistically significant differences in outcomes between the CCI and SULI alumni groups, including academic achievement, career activities, and interest in working at LBNL in the future, we applied Fisher’s exact test to our data and reported two-tailed p -values [100,101].”
Do quantitative bias analyses accurately characterize bias due to uncontrolled confounding?
Armidie, T. A., Collin, L. J., MacLehose, R. F., Ahern, T. P., & Lash, T. (2026) · Annals of Epidemiology · doi:10.1016/j.annepidem.2026.110270
Metadata
- Authors
- Greenland, S.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Greenland, Sander
- ORCID
- Greenland, Sander
- Venue
- Epidemiology
- Published
- 1990-11
- Volume
- 1
- Issue
- 6
- Pages
- 421-429
- ISSN
- 1044-3983 (ISSN-L)
- OpenAlex
- W2038826970
- Cited by
- 385
- References
- 0
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 12 · 2023: 11 · 2022: 16 · 2021: 18 · 2020: 21 · 2019: 7 · 2018: 7 · 2017: 14 · 2016: 13 · 2015: 11 · 2014: 9 · 2013: 10 · 2012: 13
- FWCI
- 5.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Keywords
- causal inference, randomization, random sampling, statistical inference, data descriptors, probability models, epidemiological study design
- MeSH terms
- Bayes Theorem, Data Interpretation, Statistical, Epidemiologic Methods, Humans, Causality, Confounding Factors, Epidemiologic, Randomized Controlled Trials as Topic
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