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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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Big Data, Data Science, and Causal Inference: A Primer for Clinicians
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“Despite these efforts, until recently, mainstream statistics has provided clinicians and researchers with few approaches to explicitly articulate, let alone to answer, causal questions (1, 8).”
BackgroundMethodCausal inference in drug discovery and development
Michoel, T., & Zhang, J. D. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103737 · Cited by 49
“● Causal machine learning aiming at individualised causal inference using high-dimensional features to subdivide population, with emerging work in medicine and healthcare 43,151 .”
BackgroundGrand Challenges of Smart Technology for Older Adults
Zhou, J., Salvendy, G., Boot, W. R., Charness, N. H., Czaja, S. J., Gao, Q., Holzinger, A., Ntoa, S., Rau, P. P., Rogers, W. A., et al. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2457003 · Cited by 54
“One particularly relevant approach is the one of providing the possibility of asking questions of “why” (Pearl, 2019), and this can be further extended to a so-called counterfactual questioning (“what would happen if”) (Guidotti, 2024; Metsch et al., 2024; Prosperi et al., 2020; Roese, 1997;…”
Tolio, T. A. M., Monostori, L., Váncza, J., & Sauer, O. (2023) · CIRP Annals · doi:10.1016/j.cirp.2023.04.091 · Cited by 41
“Big data tends to be far more focused on correlation and misses causation [185].”
BackgroundThe Causal Fairness Field Guide: Perspectives From Social and Formal Sciences
Carey, A. N., & Wu, X. (2022) · AI and Ethics · doi:10.3389/fdata.2022.892837 · Cited by 109
“In Pearl (2019), Pearl presented the causal hierarchy through the Ladder of Causation, as shown in Figure 3 .”
BackgroundIskarous, K., & Pouplier, M. (2022) · Journal of Phonetics · doi:10.1016/j.wocn.2022.101195 · Cited by 37
Min, C., Wen, G., Gou, L., Li, X., & Yang, Z. (2023) · Energy · doi:10.1016/j.energy.2023.129211 · Cited by 40
Why We Should Teach Causal Inference: Examples in Linear Regression With Simulated Data
Lübke, K., Gehrke, M., Horst, J., & Szepannek, G. (2020) · Journal of Statistics Education · doi:10.1080/10691898.2020.1752859 · Cited by 37
Ketter, W., Padmanabhan, B., Pant, G., & Raghu, T. S. (2020) · Journal of the Association for Information Systems · doi:10.17705/1jais.00631 · Cited by 42
“Limitations of traditional predictive analytics are also leading researchers to seek casual frameworks for machine learning (Pearl, 2019).”
BackgroundGuide-target mismatch effects on dCas9–sgRNA binding activity in living bacterial cells
Feng, H., Guo, J., Wang, T., Zhang, C., & Xing, X. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1295 · Cited by 36
For a heterodox computational social science
Törnberg, P., & Uitermark, J. (2021) · Big Data & Society · doi:10.1177/20539517211047725 · Cited by 50
Mechanisms of Change in Exposure Therapy for Anxiety and Related Disorders: A Research Agenda
Benito, K., Pittig, A., Abramowitz, J. S., Arch, J. J., Chavira, D. A., de Kleine, R. A., Nadai, A. S. D., Hermans, D., Hofmann, S. G., Hoyer, J., et al. (2024) · Clinical Psychological Science · doi:10.1177/21677026241240727 · Cited by 37
“Modern approaches to specifying causal models and evaluating them statistically are heavily influenced by the counterfactual framework (Pearl, 2019b).”
Zhang, X., Zhang, T., Hou, L., Liu, X., Guo, Z., Tian, Y., & Liu, Y. (2025) · Systems · doi:10.3390/systems13070581 · Cited by 36
Degrees of algorithmic equivalence between the brain and its DNN models
Schyns, P. G., Snoek, L., & Daube, C. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.09.003 · Cited by 39
“, with which algorithmic method), although of course constraining any algorithm to process specific causal features is a strong constraint on what this algorithm must do [43].”
A systematic review of machine learning-based microscopic traffic flow models and simulations
Davies, R., He, H., Hui, F., Ali, Y., & Mohammed, Q. (2025) · Communications in Transportation Research · doi:10.1016/j.commtr.2025.100164 · Cited by 41
Empowering Language Understanding with Counterfactual Reasoning
Feng, F., Zhang, J., He, X., Zhang, H., & Chua, T. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.196 · Cited by 35
An Introduction to Causal InferencePreprint
Dablander, F. (2020) · doi:10.31234/osf.io/b3fkw · Cited by 35
“Following Pearl (2019b), I distinguish three “levels” of causal inference.”
BackgroundInfluentialQuantifying causality in data science with quasi-experiments
Liu, T., Ungar, L., & Kording, K. P. (2021) · Nature Computational Science · doi:10.1038/s43588-020-00005-8 · Cited by 37
Digital Twins in eHealth – : Prospects and Challenges Focussing on Information Management
Lutze, R. (2019) · IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) · doi:10.1109/ice.2019.8792622 · Cited by 32
“But, even for these techniques, AI pioneer Judea Pearl pointed out in a recent article [33] that most current AI techniques cover only the basic association level of causal reasoning.”
BackgroundMachine learning and structural econometrics: contrasts and synergies
Iskhakov, F., Rust, J. P., & Schjerning, B. (2020) · Econometrics Journal · doi:10.1093/ectj/utaa019 · Cited by 35
“Pearl (2019) also raised the question of robustness: if the environment that an actor–critic algorithm is ‘expecting’ changes sufficiently, will its performance break down and result in counterproductive forecasts and recommendations?”
BackgroundWhat would have happened? Counterfactuals, hypotheticals and causal judgements
Gerstenberg, T. (2022) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2021.0339 · Cited by 39
“To explain the pattern of causal judgements across the different video clips, a model requires the causal knowledge and computational capacities on level III of Pearl’s hierarchy.”
BackgroundInfluentialGenerative causal explanations of black-box classifiersPreprint
O’Shaughnessy, M. R., Canal, G., Connor, M., Davenport, M. A., & Rozell, C. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.13913 · Cited by 38
“Because explanation methods seek to answer “why” and “how” questions that use the language of cause and effect [3, 4], causal reasoning has played an increasingly important role in designing explanation frameworks [5].”
BackgroundSocially Responsible AI Algorithms: Issues, Purposes, and ChallengesPreprint
Cheng, L., Varshney, K. R., & Liu, H. (2021) · Journal of Artificial Intelligence Research · doi:10.48550/arxiv.2101.02032 · Cited by 249
“Causal relationship, however, is expected to be invariant and robust across environments (Pearl, 2019, 2009).”
BackgroundYu, R., Zheng, Y., Abdel-Aty, M. A., & Gao, Z. (2019) · Accident Analysis & Prevention · doi:10.1016/j.aap.2019.01.022 · Cited by 32
Evans, J. (2020) · Journal of Social Computing · doi:10.23919/jsc.2020.0002 · Cited by 35
“This has led social scientists to recruit insights from machine learning into their quest for causal explanation [53] as many experts in machine learning have come to see causality as the next great frontier in their field [54] in a quest to yield simulations that effectively redesign society.”
Background
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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