Data Skeptic Graphs for Causal AI
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May 24, 2025 Utkarshani Jaimini, a grad student at the University of South Carolina's Artificial Intelligence Institute, focuses on causal neurosymbolic AI. She explores how AI can distinguish cause from correlation using knowledge graphs. Jaimini discusses the practical implications for healthcare, including personalized models for conditions like pediatric asthma. Additionally, she addresses challenges in causal inference and the integration of weights in link prediction, all while emphasizing the importance of explainability in AI systems.
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Backdoor Paths Cause Spurious Links
- Backdoor paths cause spurious correlations by connecting cause and effect through confounders.
- Recognizing and mitigating these paths improves causal inference in knowledge graphs.
Smoking and Gene Backdoor Path
- Smoking lung cancer link can be confounded by a genetic factor causing both behaviors.
- This illustrates the importance of accounting for backdoor paths in causal analysis.
Asthma Research Motivated Causal LP
- Utkarshani's asthma research showed correlations with pollution but not clear causation.
- She combined causal Bayesian networks with knowledge graphs to enrich causal reasoning in AI.