Cited by 659Bayesian Modeling and Causal InferenceLogic, Reasoning, and KnowledgeAdvanced Causal Inference Techniques

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The seven tools of causal inference, with reflections on machine learning

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Pearl, Judea·2019·Communications of the ACM

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Abstract

The kind of causal inference seen in natural human thought can be "algorithmitized" to help produce human-level machine intelligence.

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The central role of the propensity score in observational studies for causal effects
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Imagination
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Memory
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Perception
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Dedication
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Authors: Pearl, J.
Affiliations: University of California, Los Angeles (US)
Corresponding author: Pearl, Judea
Venue: Communications of the ACM
Publisher: Association for Computing Machinery (ACM)
Published: 2019-02-21
Volume: 62
Issue: 3
Pages: 54-60
ISSN: 0001-0782 (ISSN-L)
1557-7317
DOI: 10.1145/3241036
OpenAlex: W2917286209
Cited by: 659
References: 46
Citations by year: 2026: 78 · 2025: 81 · 2024: 91 · 2023: 98 · 2022: 102 · 2021: 118 · 2020: 67 · 2019: 24
FWCI: 35.88
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: Communications of the ACM (publishedVersion)
Funders: National Science Foundation (1704932, 1302448, 1527490)
Defense Advanced Research Projects Agency (#W911NF-16-057)
University of California, Los Angeles
Advanced Research Projects Agency
Office of Naval Research (N00014-17-S-B001, N00014)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords: causal inference, causal discovery, causal discovery algorithms, machine learning, philosophy of causation

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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What counts as a claim ›How we measure confidence ›How we check this ›

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The seven tools of causal inference, with reflections on machine learning

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Claims the paper makes

Claims other papers make about this one

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