Coarse-to-Fine Learning of Dynamic Causal Structures
arXiv:2602.22532v1 Announce Type: new Abstract: Learning the dynamic causal structure of time series is a challenging problem. Most existing approaches rely on distributional or structural invariance to uncover underlying causal dynamics, assuming stationary or partially stationary causality. However, these assumptions often conflict with the complex, time-varying causal relationships observed in real-world systems. This motivates the need for methods that address fully dynamic causality, where both instantaneous and lagged dependencies evolve over time. Such a setting poses significant challenges for the efficiency and stability of causal discovery. To address these challenges, we introduce DyCausal, a dynamic causal structure learning framework. DyCausal leverages convolutional networks to capture causal patterns within coarse-grained time windows, and then applies linear interpolation to refine causal structures at each time step, thereby recovering fine-grained and time-varying ca
arXiv:2602.22532v1 Announce Type: new Abstract: Learning the dynamic causal structure of time series is a challenging problem. Most existing approaches rely on distributional or structural invariance to uncover underlying causal dynamics, assuming stationary or partially stationary causality. However, these assumptions often conflict with the complex, time-varying causal relationships observed in real-world systems. This motivates the need for methods that address fully dynamic causality, where both instantaneous and lagged dependencies evolve over time. Such a setting poses significant challenges for the efficiency and stability of causal discovery. To address these challenges, we introduce DyCausal, a dynamic causal structure learning framework. DyCausal leverages convolutional networks to capture causal patterns within coarse-grained time windows, and then applies linear interpolation to refine causal structures at each time step, thereby recovering fine-grained and time-varying causal graphs. In addition, we propose an acyclic constraint based on matrix norm scaling, which improves efficiency while effectively constraining loops in evolving causal structures. Comprehensive evaluations on both synthetic and real-world datasets demonstrate that DyCausal achieves superior performance compared to existing methods, offering a stable and efficient approach for identifying fully dynamic causal structures from coarse to fine.
Executive Summary
This article introduces DyCausal, a dynamic causal structure learning framework that tackles the challenging problem of learning time-varying causal relationships in real-world systems. Unlike existing approaches that assume stationary or partially stationary causality, DyCausal leverages convolutional networks to capture causal patterns within coarse-grained time windows and then refines causal structures at each time step using linear interpolation. The proposed framework also incorporates an acyclic constraint based on matrix norm scaling to improve efficiency and prevent loops in evolving causal structures. Comprehensive evaluations demonstrate that DyCausal achieves superior performance compared to existing methods. The framework has significant implications for understanding complex systems and decision-making in fields such as economics, finance, and healthcare.
Key Points
- ▸ DyCausal leverages convolutional networks to capture causal patterns within coarse-grained time windows.
- ▸ Linear interpolation is used to refine causal structures at each time step.
- ▸ An acyclic constraint based on matrix norm scaling is proposed to improve efficiency and prevent loops in evolving causal structures.
Merits
Robustness to Non-Stationarity
DyCausal's ability to capture time-varying causal relationships makes it robust to non-stationarity in real-world systems.
Efficient Causal Discovery
The proposed acyclic constraint and linear interpolation techniques improve the efficiency and stability of causal discovery.
Demerits
Computational Complexity
The framework's reliance on convolutional networks and linear interpolation may increase computational complexity, especially for large datasets.
Limited Generalizability
The framework's performance may be limited to specific domains or applications, and further evaluation is needed to assess its generalizability.
Expert Commentary
The introduction of DyCausal is a significant advancement in the field of causal discovery. The framework's ability to capture time-varying causal relationships and its efficient causal discovery techniques make it a valuable tool for understanding complex systems. However, the framework's computational complexity and limited generalizability are areas that require further attention. Additionally, the framework's reliance on convolutional networks and linear interpolation may limit its applicability to specific domains or applications. Nevertheless, the potential implications of DyCausal are vast, and further research is needed to fully explore its capabilities.
Recommendations
- ✓ Future research should focus on evaluating DyCausal's performance on a wider range of datasets and applications.
- ✓ The framework's computational complexity should be addressed through optimization techniques or alternative methods.
