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Causal inference in AI education: A primer

Author

Listed:
  • Forney Andrew

    (Department of Computer Science, Loyola Marymount University, Los Angeles, CA 90045, USA)

  • Mueller Scott

    (Department of Computer Science, University of California, Los Angeles, CA 90095, USA)

Abstract

The study of causal inference has seen recent momentum in machine learning and artificial intelligence (AI), particularly in the domains of transfer learning, reinforcement learning, automated diagnostics, and explainability (among others). Yet, despite its increasing application to address many of the boundaries in modern AI, causal topics remain absent in most AI curricula. This work seeks to bridge this gap by providing classroom-ready introductions that integrate into traditional topics in AI, suggests intuitive graphical tools for the application to both new and traditional lessons in probabilistic and causal reasoning, and presents avenues for instructors to impress the merit of climbing the “causal hierarchy” to address problems at the levels of associational, interventional, and counterfactual inference. Finally, this study shares anecdotal instructor experiences, successes, and challenges integrating these lessons at multiple levels of education.

Suggested Citation

  • Forney Andrew & Mueller Scott, 2022. "Causal inference in AI education: A primer," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 141-173, January.
    • Handle: RePEc:bpj:causin:v:10:y:2022:i:1:p:141-173:n:1
    DOI: 10.1515/jci-2021-0048
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    References listed on IDEAS

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