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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Background
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BackgroundMethod
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Causal Intervention for Mitigating Name Bias in Machine Reading Comprehension
Zhu, J., Wu, S., Zhang, X., Hou, Y., & Feng, Z. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.812 · Cited by 6
“It not only is a framework for interpreting data, but also provides causal modeling tools and solutions to achieve intended goals by estimating causal effect (Pearl, 2019).”
Background
Modeling confidence in causal judgments.
O’Neill, K. J., Henne, P., Pearson, J., & Brigard, F. D. (2024) · Journal of Experimental Psychology General · doi:10.1037/xge0001615 · Cited by 6
Toward Automated Support of Complaint Handling Processes: An Application in the Medical Technology Industry
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Complex Causal Extraction of Fusion of Entity Location Sensing and Graph Attention Networks
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Impact of forest type and age on shallow landslide susceptibility: a case study from the 2017 heavy rainfall in northern Kyushu, Japan
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“…the outcome (i.e. shallow landslide susceptibility) and the variable (i.e. land cover), they cannot adequately evaluate the impact on the outcome when the variable is changed (Hernán et al. 2019; Pearl 2019; Liu et al. 2021), because the influence of confounding factors cannot be eliminated.”
Background
Systematic Human Learning and Generalization From a Brief Tutorial With Explanatory Feedback
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“Based in part on the findings from our experiments as well as work by others (Ahn et al., 1992; Chi et al., 1989; Lombrozo, 2006; Pearl, 2019; Rumelhart, 1980), we believe that one important part of helping models make better use of their context will be to find more human-like ways to encourage…”
Result
Estimating Causal Treatment Effects of Femoral and Tibial Derotational Osteotomies on Foot Progression in Children with Cerebral PalsyPreprint
Schwartz, M. H., Kainz, H., & Georgiadis, A. G. (2021) · medRxiv · doi:10.1101/2021.03.04.21252476 · Cited by 6
“An RCT is the gold standard for measuring causal effects, but it is not the only means for obtaining an causal estimate (12).”
Background
The Einstein effect: Global evidence for scientific source credibility effects and the influence of religiosityPreprint
Hoogeveen, S., Haaf, J. M., Bulbulia, J. A., Ross, R. M., McKay, R., Altay, S., Bendixen, T., Berniûnas, R., Cheshin, A., Gentili, C., et al. (2020) · doi:10.31234/osf.io/sf8ez · Cited by 6
Quantifying Causal Pathways of Teleconnections
Kretschmer, M. (2022) · EGU General Assembly Conference Abstracts · doi:10.5194/egusphere-egu22-8246 · Cited by 9
Robust agents learn causal world models
Richens, J., & Everitt, T. (2024) · arXiv · doi:10.48550/arxiv.2402.10877 · Cited by 5
“Causal reasoning is foundational to human intelligence, and has been conjectured to be necessary for achieving human level AI (Pearl, 2019).”
Background
From Econometrics to Machine Learning: Transforming Empirical Asset Pricing
Shi, C. (2025) · Journal of Economic Surveys · doi:10.1111/joes.70002 · Cited by 10
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A comparison of causal discovery and explainable AI (XAI) for image datasets
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Understanding Requirements for Technology-Supported Healthy Aging
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“This approach is often done in the context of graphical models such as Bayesian or Gaussian Networks, and builds upon long research of Judea Pearl (i.e., Pearl, 2000).”
BackgroundMethod
Event Mining for Explanatory Modeling
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“…the observational distribution, SCE is more intuitive for machine learning researchers who are more accus‐ tomed to thinking while considering estimating func‐ tions rather than probability distributions (Pearl, 2019). where f i denotes a function and U i represents an exog‐ enous variable.”
Background
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“Human theorising is a sensible and arguably necessary element to identify causal structure (Pearl, 2018; Spirtes, 2010), but machine learning procedures such as constraint based, score based and hybrid algorithms (Koller & Friedman, 2009) can be a useful supplementary method to assist in…”
Method
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“and perform counterfactual reasoning [3]–[5] through causal”
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“Based on the work of Pearl [37,38], we started by building a directed acyclic graph (DAG) to represent the causal relationships of our measured variables, as shown in Figure 2.”
Method
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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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The seven tools of causal inference, with reflections on machine learning

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