AI RESEARCH
MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Data
arXiv CS.LG
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ArXi:2410.18918v2 Announce Type: replace-cross Causal discovery in real-world systems, such as biological networks, is often complicated by feedback loops and incomplete data. Standard algorithms, which assume acyclic structures or fully observed data, struggle with these challenges. To address this gap, we propose MissNODAG, a differentiable framework for learning both the underlying cyclic causal graph and the missingness mechanism from partially observed data, including data missing not at random.