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
Open accessAbstract
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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Citations
Cites
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
(1988) · Cited by 16,582
Self-citationAdvances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
Self-citationCAUSALITY: MODELS, REASONING AND INFERENCE
Hitchcock, C., & Pearl, J. (2001) · The Philosophical Review · doi:10.2307/3182612 · Cited by 4,826
Self-citationCausation, Prediction, and Search
Spirtes, P., Glymour, C., & Scheines, R. (2001) · doi:10.7551/mitpress/1754.001.0001 · Cited by 4,521
Causality: models, reasoning, and inference
(2000) · Choice Reviews Online · doi:10.5860/choice.38-1494a · Cited by 2,968
Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015) · Science · doi:10.1126/science.aab3050 · Cited by 2,817
Causal diagrams for empirical research
PEARL, J. (1995) · Biometrika · doi:10.1093/biomet/82.4.669 · Cited by 2,355 · cited 2×
“…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].”
Self-citation
Research works printed but not linked: 10
- Balke A., 1994, Proceedings of the 12th National Conference on Artificial Intelligence (Seattle, WA, July 31-Aug. 4)
- 10.5555/3086758 · DOI
- Jaber A., 2018, Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence
- Morgan S.L., 2015, Counterfactuals and Causal Inference: Methods and Principles for Social Research (Analytical Methods for Social Research)
- Pearl J., 1988, Morgan Kaufmann
- 10.5555/3202377 · DOI
- Porta M., 2014, Oct. 17
- 10.5555/1248547.1248619 · DOI
- Tian J., 2002, Proceedings of the 18th National Conference on Artificial Intelligence (Edmonton, AB, Canada, July 28-Aug. 1). AAAI Press/MIT Press
- 10.5555/3171642.3171832 · DOI
Cited by
Sorted by citations
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.”
BackgroundMachine learning and deep learning—A review for ecologists
Pichler, M., & Hartig, F. (2023) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14061 · Cited by 576
“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).”
Jung, J., Maeda, M. M., Chang, A., Bhandari, M., Ashapure, A., & Landivar-Bowles, J. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2020.09.003 · Cited by 472
Explainable AI Methods - A Brief Overview
Holzinger, A., Saranti, A., Molnar, C., Biecek, P., & Samek, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-04083-2_2 · Cited by 518
“Therefore, it will be important to measure the quality of explanations so that an xAI method achieves a certain level of causal understanding [65] for a user with effectiveness, efficiency and satisfaction in a given context of use”
MethodCRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories
Martínez‐Plumed, F., Contreras-Ochando, L., Ferri, C., Hernández‐Orallo, J., Kull, M., Lachiche, N., Ramírez-Quintana, M. J., & Flach, P. A. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2962680 · Cited by 598
“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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