Causal Neuro-Symbolic AI: A Synergy Between Causality and Neuro-Symbolic Methods

Author(s)

Utkarshani Jaimini, University of South Carolina - ColumbiaFollow
Cory HensonFollow
Amit Sheth, University of South Carolina - ColumbiaFollow

ORCID iD

Jaimini: https://orcid.org/0000-0002-1168-0684

Henson: https://orcid.org/0000-0003-3875-3705

Sheth: https://orcid.org/0000-0002-0021-5293

Document Type

Article

Abstract

Causal Neuro-Symbolic AI combines the benefits of causality with Neuro-Symbolic Artificial Intelligence (NeSyAI). More specifically, it (1) enriches NeSyAI systems with explicit representations of causality, (2) integrates causal knowledge with domain knowledge, and (3) enables the use of NeSyAI techniques for causal AI tasks. The explicit causal representation yields insights that predictive models may fail to analyze from observational data. It can also assist people in decision-making scenarios where discerning the cause of an outcome is necessary to choose among various interventions.

Publication Info

Preprint version IEEE Intelligent Systems, Volume 39, Issue 2, 2024.

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APA Citation

Jaimini, U., Henson, C., & Sheth, A. (2024). Causal Neuro-Symbolic AI: A synergy between Causality and Neuro-Symbolic methods. IEEE Intelligent Systems, 39(2).