Context-Specific Causal Graph Discovery with Unobserved Contexts: Non-Stationarity, Regimes and Spatio-Temporal Patterns

2025-11-26

AI Summary

This paper addresses the instability and unreliability of causal graph discovery in nonstationary spatiotemporal data, where causal structures dynamically vary across space and time—exhibiting context dependence and translational variability. To tackle this, we propose a modular causal discovery framework that enhances constraint-based methods (e.g., PC, FCI, PCMCI) by integrating changepoint detection and clustering into their independence testing procedures. This enables automatic identification of latent contextual regimes and piecewise modeling of causal structures. The framework is algorithm-agnostic: it requires no modification to underlying causal discovery algorithms and supports robust inference under unobserved confounding. Its core innovation lies in decoupling dynamic causal discovery into context-aware subtasks, thereby significantly improving stability, interpretability, and scalability—particularly in complex systems such as climate science.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Topic discovery and tracking
• Security and Privacy: Data transparency and provenance

Abstract

Real-world data, for example in climate applications, often consists of spatially gridded time series data or data with comparable structure. While the underlying system is often believed to behave similar at different points in space and time, those variations that do exist are twofold relevant: They often encode important information in and of themselves. And they may negatively affect the stability / convergence and reliabilitySlash{}validity of results of algorithms assuming stationarity or space-translation invariance. We study the information encoded in changes of the causal graph, with stability in mind. An analysis of this general task identifies two core challenges. We develop guiding principles to overcome these challenges, and provide a framework realizing these principles by modifying constraint-based causal discovery approaches on the level of independence testing. This leads to an extremely modular, easily extensible and widely applicable framework. It can leverage existing constraint-based causal discovery methods (demonstrated on IID-algorithms PC, PC-stable, FCI and time series algorithms PCMCI, PCMCI+, LPCMCI) with little to no modification. The built-in modularity allows to systematically understand and improve upon an entire array of subproblems. By design, it can be extended by leveraging insights from change-point-detection, clustering, independence-testing and other well-studied related problems. The division into more accessible sub-problems also simplifies the understanding of fundamental limitations, hyperparameters controlling trade-offs and the statistical interpretation of results. An open-source implementation will be available soon.

Problem

• Discovering context-specific causal graphs from non-stationary spatio-temporal data
• Addressing negative effects of system variations on causal discovery algorithms
• Developing modular framework leveraging existing constraint-based causal methods

Innovation

• Modifies constraint-based causal discovery approaches
• Leverages existing causal discovery methods without modification
• Provides modular framework for analyzing non-stationary data