The new science of cause and effect, with reflections on data science and artificial intelligence
Abstract
The past three decades have seen the development of powerful tools for modeling and computing causal relationships which may have major impact on data science. My talk will illustrate how these tools work in seven tasks: 1. Encoding causal assumptions in transparent and testable way 2. Predicting the effects of actions and policies 3. Computing counterfactuals and finding causes of effects 4. Computing direct and indirect effects (Mediation) 5. Integrating data from diverse sources. 6. Recovering from missing data 7. Discovering causal relations from data. A friendly, non-technical account of these ideas is available in: “The Book of Why: the new science of cause and effect,” Judea Pearl and Dana MacKenzie, (Basic Books, 2018). http://bayes.cs.ucla.edu/WHY/.
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Classical classifiers treat intuitive co-occurrences as true causal links and infer an observed object's semantic attributes from context features.
“Classical classifiers regard the intuitive co-occurrences as the real causal links, and recognize the semantic attributes of observed objects based on the context features, which can be denoted as ( | ), i.e., the link → .”
From Unbiased Embodied Visual Representation Learning with Causal Inference and Cross-Modality Alignment (2025) · cites this paper as [42]
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Cited by 4
- Authors
- Pearl, J.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Pearl, Judea
- Venue
- 2019 IEEE International Conference on Big Data (Big Data)
- Publisher
- IEEE
- Published
- 2019-12
- Pages
- 4-4
- OpenAlex
- W3008416861
- Cited by
- 4
- References
- 0
- Citations by year
- 2022: 1 · 2020: 3
- Type
- conference abstract
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on Big Data (BigData) (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Keywords
- causal inference, causal models, counterfactual reasoning, mediation analysis, policy evaluation, missing data, causal discovery, data integration, artificial intelligence, data science
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