RECLAIM: Cyclic Causal Discovery Amid Measurement Noise
This addresses a fundamental problem in science and engineering, such as genomics, by handling cycles and noise, though it is incremental as it builds on existing causal discovery methods.
The authors tackled the problem of causal discovery in settings with cyclic relationships and measurement noise, proposing RECLAIM, a framework that learns causal graphs via expectation-maximization with residual normalizing flows, and demonstrated its efficacy on synthetic and real-world datasets.
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.