The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
The seven tools of causal inference, with reflections on machine learning
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The kind of causal inference seen in natural human thought can be "algorithmitized" to help produce human-level machine intelligence.
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“Causal reasoning is foundational to human intelligence, and has been conjectured to be necessary for achieving human level AI (Pearl, 2019).”
From Robust agents learn causal world models (2024) · cites this paper as Pearl, 2019
In the citing paper
“ML can play a role in causal inference [41,340,619], including in the context of policy problems [40,453] and in climate-relevant scenarios such as estimating the effects of temperature on human mortality [356] and the effects of World Bank projects on vegetative cover [883].”
From Tackling climate change with machine learning (2023) · cites this paper as [619]
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“…engine is available, called do - calculus , which predicts the effect of policy interventions whenever feasible, and exits with failure whenever predictions cannot be ascertained with the specified assumptions [Bareinboim and Pearl 2012; Pearl 1995; Shpitser and Pearl 2008; Tian and Pearl 2002].”
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Cited by
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Neurosymbolic AI: the 3rd wave
Garcez, A. D., & Lamb, L. C. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10448-w · Cited by 847
“In Pearl (2019), Pearl proposes a hierarchy consisting of three levels: association, intervention and counterfactual reasoning, and claims that ML is only capable of achieving association.”
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“Moreover, a pervasive concern is that ML models are trained for prediction, but the best predictive model does not necessarily correspond to the causal model (Breiman, 2001b; Pearl, 2019 , 2021; Box 4).”
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“Causal inference from data focuses on answering questions of the type “what if” and relies on methods that incorporate causal knowledge (such as the Structural Causal Models [46], the Potential Outcomes Framework [47] or the Linear non-Gaussian acyclic models [48]).”
Method
Metadata
- Authors
- Pearl, J.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Pearl, Judea
- Venue
- Communications of the ACM
- Published
- 2019-02-21
- Volume
- 62
- Issue
- 3
- Pages
- 54-60
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- 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
- 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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