Tagged for Direction: Pinning Down Causal Edge Directions with Precision

2025-06-24

AI Summary

Traditional causal discovery methods often assume homogeneous variable types, limiting the robustness and flexibility of causal direction inference. To address this, we propose a multi-label-based causal direction inference framework: each variable is assigned semantically meaningful labels; a label graph is constructed to model higher-order directional relationships among labels; and label preferences—propagated from already-oriented edges—are leveraged to guide orientation decisions for undirected edges. This approach relaxes the single-variable-type assumption and tightly couples causal structure learning with interpretable label semantics. Experiments across multiple benchmark datasets demonstrate significant improvements in causal direction identification accuracy. Moreover, the inferred label-level directional patterns align with domain knowledge, thereby enhancing the expressiveness, robustness, and interpretability of learned causal models.

Technology Category

Machine Learning: Causal LearningReasoning under Uncertainty: CausalityKnowledge Representation and Reasoning: Action, Change, and Causality

Application Category

Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semanticsGraph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphsWeb Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Not every causal relation between variables is equal, and this can be leveraged for the task of causal discovery. Recent research shows that pairs of variables with particular type assignments induce a preference on the causal direction of other pairs of variables with the same type. Although useful, this assignment of a specific type to a variable can be tricky in practice. We propose a tag-based causal discovery approach where multiple tags are assigned to each variable in a causal graph. Existing causal discovery approaches are first applied to direct some edges, which are then used to determine edge relations between tags. Then, these edge relations are used to direct the undirected edges. Doing so improves upon purely type-based relations, where the assumption of type consistency lacks robustness and flexibility due to being restricted to single types for each variable. Our experimental evaluations show that this boosts causal discovery and that these high-level tag relations fit common knowledge.

Problem

• Improving causal edge direction precision using tags
• Enhancing robustness over single-type causal discovery
• Leveraging tag relations to direct undirected edges

Innovation

• Assigns multiple tags to each variable
• Uses tag relations to direct edges
• Improves robustness over single-type methods