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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Statistical modelling of determinants of child stunting using secondary data and Bayesian networks: a UKRI Global Challenges Research Fund (GCRF) Action Against Stunting Hub protocol paper
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“22 Bayesian networks (BNs) by comparison offer an opportunity to build holistic causal models, based on the current understanding of the complex factors determining child stunting and their inter-relationship.”
Background
Causal Machine Learning Analysis of Radiation-Induced Leukemia and Solid Tumor Incidence in Japanese Atomic Bomb SurvivorsPreprint
Shuryak, I., Liu, Z., Wang, E., Ullrich, R. L., & Brenner, D. J. (2024) · medRxiv · doi:10.1101/2024.04.30.24306639 · Cited by 3
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Hakizimana, G., & Ledezma Espino, A. (2025) · Algorithms · doi:10.3390/a18060306 · Cited by 2
“Several scholars have advocated integrating causal explanation theories, particularly Judea Pearl’s framework of causal inference [127], which models relationships using Bayesian networks [128] and supports counterfactual reasoning through directed acyclic graphs (DAGs).”
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“The SCM framework provides a mathematical foundation for representing causal relationships between variables in complex systems [17,18].”
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“Causality has gained increased attention as a foundation for trustworthy robot autonomy [5], [6], addressing key limitations of machine learning such as domain transfer and lack of interpretability or accountability [7], [8].”
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Dattijo, A., & Jo, S. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00439-x · Cited by 3
“This includes developing systems capable of causal inference, which advances beyond pattern matching to understand cause-and-effect relationships in complex environments [36].”
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Empirical evidence of direct and indirect relations between environmental pressure and conflict
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Mapping the landscape of AI-empowered psychology: a topic modeling-based bibliometric analysis
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“This shift toward purely predictive approaches risks transforming psychological science into what Pearl ( 2019 ) calls a “data-centric” enterprise, where researchers become primarily occupied with fitting complex functions to observed patterns rather than understanding the generative mechanisms…”
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“For an introduction to causal inference see Pearl (2019).”
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Igoche, B. I., Matthew, O., Bednár, P., & GEGOV, A. E. (2024) · IEEE International Conference on Intelligent Systems · doi:10.1109/is61756.2024.10705239 · Cited by 2
“Pearl [11] introduced the concept of Structural Causal Models (SCMs), a formal framework for representing and reasoning about causal relationships.”
Background
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“This provides a third level of reasoning and completes Pearl ’ s (2019) “ ladder of causation ” (Table 1).”
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Processing Use Case Scenarios and Text in a Formal Style as Inputs for TFM-based Transformations
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“In the formal text (instructions, descriptions of processing, etc.) they usually have a form “if/when <a cause occurs> then <an effect occurs>” as well as temporal priorities can be helpful in distinguishing them (Solstad and Bott, 2017; Pearl, 2019).”
Background
What Can the Millions of Random Treatments in Nonexperimental Data Reveal About Causes?
Ribeiro, A. F., Neffke, F. M. H., & Hausmann, R. (2022) · SN Computer Science · doi:10.1007/s42979-022-01319-2 · Cited by 2
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“Although this in the beginning may be more as a form of identifying potential hypotheses that later can be further examined using standard methods, there are also developments in the direction of thinking causally in machine learning (57).”
Background
Identifying Causal Influences on Publication Trends and Behavior: A Case Study of the Computational Linguistics Community
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Buchard, A., & Richens, J. G. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_28-1 · Cited by 2
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Hattori, R., Ota, Y., Fujii, T., & Nakajima, H. (2021) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc52423.2021.9659060 · Cited by 2
“To overcome the lack of explainability and the need to understand cause-effect connections, Pearl argued that causal reasoning is an indispensable component and causal modeling is an effective method for these problems [12].”
Method

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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