AI RESEARCH
Causal-Audit: A Framework for Risk Assessment of Assumption Violations in Time-Series Causal Discovery
arXiv CS.LG
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ArXi:2604.02488v1 Announce Type: new Time-series causal discovery methods rely on assumptions such as stationarity, regular sampling, and bounded temporal dependence. When these assumptions are violated, structure learning can produce confident but misleading causal graphs without warning. We