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Causal neurosymbolic AI (NeSyAI) combines the benefits of causality with 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.
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Utkarshani Jaimini
University of South Carolina
Cory Henson
Robert Bosch (India)
Amit Sheth
Université Claude Bernard Lyon 1
IEEE Intelligent Systems
University of South Carolina
Robert Bosch (United States)
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Jaimini et al. (Wed,) studied this question.
synapsesocial.com/papers/68e6c1d6b6db643587640fba — DOI: https://doi.org/10.1109/mis.2024.3395936
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