AI RESEARCH
RECLAIM: Cyclic Causal Discovery Amid Measurement Noise
arXiv CS.LG
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ArXi:2603.20585v1 Announce Type: new Uncovering causal relationships is a fundamental problem across science and engineering. However, most existing causal discovery methods assume acyclicity and direct access to the system variables -- assumptions that fail to hold in many real-world settings. For instance, in genomics, cyclic regulatory networks are common, and measurements are often corrupted by instrumental noise. To address these challenges, we propose RECLAIM, a causal discovery framework that natively handles both cycles and measurement noise.