Statistics and causal inference: A review

Pearl, Judea

doi:10.1007/bf02595718

Cited by 144Advanced Causal Inference TechniquesBayesian Modeling and Causal InferencePhilosophy and History of Science

Statistics and causal inference: A review

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DOI 10.1007/bf02595718

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The standard decision to assume independence among certain statistical parameters and not others (in a Bayesian prior) relies on causal information.
“Likewise, the standard decision to assume independence among certain statistical parameters and not others (in a Bayesian prior) rely on causal information (see discussions with Joseph Kadane and Serafin Moral (Pearl, 2003)).”
From Causal inference in statistics: An overview (2009) · cites this paper as Pearl, 2003

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Authors: Pearl, J.
Affiliations: University of California, Los Angeles (US)
Corresponding author: Pearl, Judea
Venue: Test
Publisher: Springer Science and Business Media LLC
Published: 2003-12
Volume: 12
Issue: 2
Pages: 281-345
ISSN: 1133-0686 (ISSN-L)
1863-8260
DOI: 10.1007/bf02595718
OpenAlex: W2162753814
Cited by: 144
References: 127
Citations by year: 2026: 5 · 2025: 9 · 2024: 8 · 2023: 8 · 2022: 8 · 2021: 5 · 2020: 8 · 2019: 6 · 2018: 4 · 2017: 3 · 2016: 5 · 2015: 6 · 2014: 9 · 2013: 6 · 2012: 5
FWCI: 4.47
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Test (publishedVersion)
RePEc: Research Papers in Economics (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 and History of Science
Keywords: causal inference, causal assumptions, confounding adjustment, causal effect estimation, counterfactuals, probabilistic graphical models, multivariate analysis

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