The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
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]
Citations
Cites
Modeling and variable selection in epidemiologic analysis
Greenland, S. (1989) · American Journal of Public Health · doi:10.2105/ajph.79.3.340 · Cited by 2,208
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Cited by
Sorted by citations
Items 301–321 of 321
Negatives about positivity and consistency as conditions for causal inference
Greenland, S., & Cole, S. R. (2026) · American Journal of Epidemiology · doi:10.1093/aje/kwag093
Self-citationCognitive-driven hierarchical reasoning for event causality identification
Yuan, J., Xu, Y., Zhou, W., Yu, M., Sun, X., Zhang, L., & Luo, J. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115914
What Is Wrong with Rerandomization in Randomized Field Experiments?
Maziarz, M. (2026) · Philosophy of Science · doi:10.1017/psa.2026.10203
“…with finite samples, the average treatment effect estimate ( d ATE ) remains an unbiased estimate of the true causal effect ( ATE true ) (Greenland 1990; Neyman 1990[1923]), but the price for the stochastic distribution of confounders is the introduction of noise ( ε ) (Mutz et al.…”
Milan, N., Woolley, K. A., Master, Z., & Feeley, B. T. (2025) · Orthopaedic Journal of Sports Medicine · doi:10.1177/23259671251374306
Treatment group-specific inferences in Phase III Randomized Oncology Trials
Sherry, A., Passy, A., Jaoude, J. A., Lin, T. A., Kouzy, R., Msaouel, P., & Ludmir, E. (2025) · Acta oncologica · doi:10.2340/1651-226x.2025.42663
“By removing selection bias in the enrollment of participants, random sampling procedures provide robust statistical license for treatment group-specific inferences [14].”
Avançando no Uso de Anticoagulantes Orais Diretos no Manejo do Trombo Ventricular Esquerdo
Carvalho, P. (2024) · Arquivos Brasileiros de Cardiologia · doi:10.36660/abc.20240409
“13 Especially considering that VKAs are associated with drug and food interactions, unpredictable dose-response, and a narrow therapeutic range, necessitating close monitoring of prothrombin time.”
Serpico, D., & Maziarz, M. (2023) · European Journal for Philosophy of Science · doi:10.1007/s13194-023-00559-0 · Cited by 2
“Suppose now that the first admission is massively compromised and is certain to die within 72 hours of admission, whereas the second is a mild case and is certain to survive, whether or not either of them receives lidocaine therapy (1990, p. 421).”
Jenicek, M. (2019) · Foundations of Evidence-Based Medicine · doi:10.1201/9780429198137-9
The ASA Statement and P -values in Epidemiologic Research
Sato, T. (2018) · Japanese Journal of Biometrics · doi:10.5691/jjb.38.109
“(http://edmgr.ovid.com/epid/accounts/ifauth.htm, 2017 年 12 月 4 日アクセス) Jpn J Biomet Vol. 38, No. 2, 2017 ことに強く否定的であり,推定精度をしめす信頼区間の報告を勧めている.一方で,検定の使用 を完全に否定しているわけではなく,モデリングに関する意思決定(交互作用やトレンドの検定) やゲノム研究に関する検定は受け入れるとしている. 検定や P 値といった頻度論的手法は,ランダム化臨床試験やランダムサンプリングをともなっ…”
Reply: Observational Versus Randomized: Sliding Toward Nonevidence-Based Medicine.
Cadrin-Tourigny, J., Shohoudi, A., Roy, D., & Khairy, P. (2017) · JACC. Heart failure · doi:10.1016/j.jchf.2017.03.002
Introduction to Randomized Clinical Trials in Cardiovascular Disease
Geisler, T., Flather, M., Bhatt, D. L., & D'Agostino, R. (2012) · doi:10.1002/9781118399378.ch1
Letter to the Editor: regarding “Letter from Tsuda et al.”
Wing, S., & Richardson, D. B. (2009) · doi:10.1016/j.annepidem.2009.03.013
Re-interpreting conventional interval estimates taking into account bias and extra-variation
Höfler, M., & Seaman, S. R. (2006) · BMC Medical Research Methodology · doi:10.1186/1471-2288-6-51
“Priors at zero for the bias parameters are inappropriate if the data are not randomly sampled or not randomly assigned to groups [18].”
Evolution Of Epidemiologic Methodology From The Statistical Perspective
Nurminen, M. M. (2002) · The Internet Journal of Epidemiology · doi:10.5580/117c
Have We Lost the Meaning? Questions around Epidemiologic Practice
Paci, E. (1996) · New solutions : a journal of environmental and occupational health policy : NS · doi:10.2190/ns6.1.g
“(5) In this vein, he suggested that non-experimental research needs to "elevate the importance of data description and summarization relative to statistical inference or else adopt inferential procedures based on more realistic probability models" in order to solve the problem of the statistical fallacy of the inferen…”
BackgroundTilastomenetelmät kehittyvät - terävöityykö epidemiologinen analyysi
Läärä, E. (1991) · Sosiaalilääketieteellinen Aikakauslehti · doi:10.23990/sa.156268
“Oletus intensiteetin tai todennakoisyyden homogeenisuudesta saman "riskiryhman" yksiloiden kesken on eparealistinen (Green land 1987), suorastaan absurdi (Greenland 1990).”
InfluentialMak, T. (2013) · Spiral (Imperial College London) · doi:10.25560/11666
“…the beginning of this thesis, although randomization or random sampling are often not employed in observational studies, standard practice in quantifying uncertainty has relied heavily on methodology developed for situations involving some form of randomization or random sampling (Greenland, 1990).”
BackgroundInfluentialSystematic Reviews and Meta-Analyses in Surgery
Panesar, S., Siow, W., & Athanasiou, T. (2010) · Springer eBooks · doi:10.1007/978-3-540-71915-1_30
Mistaken Identity: P -values in Epidemiology
Moye, L. (2006) · Springer eBooks · doi:10.1007/978-0-387-46212-7_5
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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