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Evaluating network interventions requires understanding how treatment affects people through their social relationships. Counting treated neighbors without distinguishing supportive and antagonistic ties can conceal opposing influences. We define effects through positive and negative ties, their interaction, and a sign...
CICIV, a causal inference framework that integrates PC-simple-based causal feature selection with conditional instrumental variable (CIV) representation learning, is proposed that prioritized 40 breast cancer-related candidate genes and identified signals that were not captured by conventional correlation-based analyse...
TranCE (Transported Causal Effects), a doubly-robust algorithm combining an interventional outcome model, a domain density-ratio correction, and cross-fitted inference, is presented, which has the potential to improve intervention strategies in networked systems, particularly in social networks and public health.
This survey addresses hallucination mitigation through the lens of explainability, proposing a taxonomy that distinguishes between internal explainability and post hoc explainability and discusses the constructive role of hallucinations in creative and user‐driven applications.
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