Twenty-six assumptions that have to be met if single random assignment experiments are to warrant “gold standard” status: A commentary on Deaton and Cartwright
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Even though experiments are among the methods that have experienced replication problems, and even though they have very demanding requirements and assumptions, properly done experiments are less subject to some of the methodological limits of observational statistical studies.
“Even though experiments are among the methods that have experienced replication problems, and even though they have very demanding requirements and assumptions (especially field experiments: Cook, 2018), properly done experiments are less subject to some of the methodological limits of observational statistical studies.”
From Causal Inference and Policy Evaluation from Case Studies Using Bayesian Process Tracing (2023) · cites this paper as Cook, 2018
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- Authors
- Cook, T. D.
- Affiliations
- Northwestern University (US)
- Corresponding author
- Cook, Thomas D.
- ORCID
- Cook, Thomas D.
- Venue
- Social Science & Medicine
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 210
- Pages
- 37-40
- ISSN
- 0277-9536 (ISSN-L)
- OpenAlex
- W2800289677
- Cited by
- 55
- References
- 1
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 6 · 2023: 9 · 2022: 2 · 2021: 1 · 2020: 22 · 2019: 3 · 2018: 2
- FWCI
- 5.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Social Science & Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Evaluation and Performance Assessment
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- causal inference, random assignment, gold standard, confounder control, propensity score matching, instrumental variables, regression discontinuity design, difference-in-differences, threats to validity, internal validity, generalizability, statistical power, measurement quality, education and health, interdisciplinary methodology
- MeSH terms
- Research Design, Randomized Controlled Trials as Topic, Comprehension
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