AI RESEARCH
SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets
arXiv CS.LG
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ArXi:2605.16620v1 Announce Type: new Learning causal relationships between variables from data is a fundamental research area with many applications across disciplines. Most existing causal discovery algorithms rely on the assumptions that (i) the underlying system is acyclic, (ii) the exogenous noise variables are Gaussian, and (iii) the intervention targets for the data-generating experiments are known. While these assumptions simplify the analysis, they are violated in real-life systems.