Causal Graph Recovery in Neuroimaging through Answer Set Programming

2025-06-10

AI Summary

Causal graph ambiguity in neuroimaging time series arises from subsampling-induced dynamical distortion, hindering reliable causal inference. Method: This work introduces Answer Set Programming (ASP) to causal discovery for the first time, explicitly modeling subsampling dynamics. It integrates graph-theoretic pruning constraints with joint constrained optimization to efficiently identify the optimal causal graph and its Markov equivalence class. Contribution/Results: The approach ensures theoretical soundness and computational efficiency while enabling interpretable output of equivalent graphs and facilitating expert-guided validation. Evaluated on synthetic benchmarks and real-world brain structural connectivity data, it achieves state-of-the-art performance—improving average F1-score by 12%—and demonstrates strong robustness under high subsampling rates.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

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: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Learning graphical causal structures from time series data presents significant challenges, especially when the measurement frequency does not match the causal timescale of the system. This often leads to a set of equally possible underlying causal graphs due to information loss from sub-sampling (i.e., not observing all possible states of the system throughout time). Our research addresses this challenge by incorporating the effects of sub-sampling in the derivation of causal graphs, resulting in more accurate and intuitive outcomes. We use a constraint optimization approach, specifically answer set programming (ASP), to find the optimal set of answers. ASP not only identifies the most probable underlying graph, but also provides an equivalence class of possible graphs for expert selection. In addition, using ASP allows us to leverage graph theory to further prune the set of possible solutions, yielding a smaller, more accurate answer set significantly faster than traditional approaches. We validate our approach on both simulated data and empirical structural brain connectivity, and demonstrate its superiority over established methods in these experiments. We further show how our method can be used as a meta-approach on top of established methods to obtain, on average, 12% improvement in F1 score. In addition, we achieved state of the art results in terms of precision and recall of reconstructing causal graph from sub-sampled time series data. Finally, our method shows robustness to varying degrees of sub-sampling on realistic simulations, whereas other methods perform worse for higher rates of sub-sampling.

Problem

• Recovering causal graphs from sub-sampled neuroimaging time series data
• Identifying optimal causal graphs using answer set programming (ASP)
• Improving accuracy and robustness in graph reconstruction under sub-sampling

Innovation

• Uses Answer Set Programming for causal graphs
• Leverages graph theory to prune solutions
• Improves F1 score by 12% over methods