Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms
Causal discovery from observational data is fundamental to statistics and machine learning, yet determining causal direction without interventions necessitates structural assumptions. Existing identifiability research primarily focuses on continuous variables under additive noise models, often neglecting mixed datasets...
Sambit Mishra, Ying-Ying Wang, Christine K. Johnson et al.
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