Generating Causal Temporal Interaction Graphs for Counterfactual Validation of Temporal Link Prediction

2026-02-02

AI Summary

Existing approaches evaluate temporal link prediction models solely based on predictive accuracy, which fails to verify whether these models capture genuine causal mechanisms. This work proposes the Causal Temporal Interaction Graph (CTIG) framework, which introduces—for the first time—a continuous-time structural equation model capable of representing both excitatory and inhibitory effects. The framework further develops a causal distance metric grounded in cross-model prediction errors, enabling quantifiable counterfactual causal evaluation. By integrating structural equation modeling, causal graph generation, and timestamp perturbation techniques, CTIG reveals significant performance degradation under controlled causal shifts, thereby effectively demonstrating its discriminative power in assessing a model’s capacity for causal reasoning.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Temporal link prediction (TLP) models are commonly evaluated based on predictive accuracy, yet such evaluations do not assess whether these models capture the causal mechanisms that govern temporal interactions. In this work, we propose a framework for counterfactual validation of TLP models by generating causal temporal interaction graphs (CTIGs) with known ground-truth causal structure. We first introduce a structural equation model for continuous-time event sequences that supports both excitatory and inhibitory effects, and then extend this mechanism to temporal interaction graphs. To compare causal models, we propose a distance metric based on cross-model predictive error, and empirically validate the hypothesis that predictors trained on one causal model degrade when evaluated on sufficiently distant models. Finally, we instantiate counterfactual evaluation under (i) controlled causal shifts between generating models and (ii) timestamp shuffling as a stochastic distortion with measurable causal distance. Our framework provides a foundation for causality-aware benchmarking.

Problem

• temporal link prediction
• causal mechanisms
• counterfactual validation
• causal temporal interaction graphs

Innovation

• causal temporal interaction graphs
• counterfactual validation
• temporal link prediction
• structural equation model
• causal distance metric