RECLAIM: Cyclic Causal Discovery Amid Measurement Noise
arXiv:2603.20585v1 Announce Type: new 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
arXiv:2603.20585v1 Announce Type: new 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.
Executive Summary
This article proposes RECLAIM, a novel causal discovery framework designed to handle cyclic causal relationships and measurement noise in real-world settings. By leveraging expectation-maximization and residual normalizing flows, RECLAIM optimizes the likelihood of observed measurements to learn the causal graph structure. The authors provide theoretical consistency guarantees for two measurement models and demonstrate the efficacy of RECLAIM on synthetic data and real-world protein signaling datasets. While the method shows promise, its applicability to more complex systems and datasets remains to be explored. The work contributes significantly to the field of causal discovery, addressing long-standing challenges in cyclic causal networks and measurement noise.
Key Points
- ▸ RECLAIM is a causal discovery framework that handles cyclic causal relationships and measurement noise.
- ▸ The method uses expectation-maximization and residual normalizing flows for tractable likelihood computation.
- ▸ Theoretical consistency guarantees are provided for two measurement models.
Merits
Handling Cyclic Causal Relationships
RECLAIM can learn causal graph structures in cyclic networks, which is a significant advancement over existing methods that assume acyclicity.
Robustness to Measurement Noise
The method can account for measurements corrupted by instrumental noise, making it suitable for real-world applications.
Theoretical Consistency Guarantees
The authors provide rigorous theoretical guarantees for the method's performance in two measurement models.
Demerits
Complexity and Scalability
While the method shows promise, its applicability to more complex systems and large-scale datasets remains to be fully explored.
Limited Evaluation on Real-World Data
While the authors demonstrate efficacy on synthetic data and a limited number of real-world protein signaling datasets, further evaluation on diverse real-world applications is necessary.
Expert Commentary
The article presents a significant contribution to the field of causal discovery, addressing long-standing challenges in cyclic causal networks and measurement noise. The proposed method, RECLAIM, shows promise in handling these complexities and provides a rigorous theoretical foundation. However, further evaluation on diverse real-world applications and exploration of its scalability to complex systems are necessary. The work has implications for both practical applications and policy decisions, highlighting the importance of causal discovery in complex systems.
Recommendations
- ✓ Future research should focus on extending the method to more complex systems and large-scale datasets.
- ✓ Further evaluation on diverse real-world applications, including but not limited to genomics, epidemiology, and finance, is necessary to establish the method's robustness and generalizability.
Sources
Original: arXiv - cs.LG
