RECLAIM: Cyclic Causal Discovery Amid Measurement Noise

• URL: http://arxiv.org/abs/2603.20585v1
• Date: Sat, 21 Mar 2026 00:53:13 GMT
• Title: RECLAIM: Cyclic Causal Discovery Amid Measurement Noise
• Abstract summary: We propose RECLAIM, a causal discovery framework that handles both cycles and measurement noise.<n>We consider two measurement models: (i) Gaussian additive noise, and (ii) a linear measurement system with additive Gaussian noise.<n>Experiments on synthetic data and real-world protein signaling datasets demonstrate the efficacy of the proposed method.
• Score: 11.865642857638527
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. RECLAIM learns the causal graph structure by maximizing the likelihood of the observed measurements via expectation-maximization (EM), using residual normalizing flows for tractable likelihood computation. We consider two measurement models: (i) Gaussian additive noise, and (ii) a linear measurement system with additive Gaussian noise. We provide theoretical consistency guarantees for both the settings. Experiments on synthetic data and real-world protein signaling datasets demonstrate the efficacy of the proposed method.

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