构建带真实因果结构的时序图，验证链接预测模型是否真正理解因果机制。
Generating Causal Temporal Interaction Graphs for Counterfactual Validation of Temporal Link Prediction
- 用结构方程模型生成支持正负影响的连续时间事件序列。
- 发现不同因果模型间预测误差随距离增大而显著上升。
- 适合关注模型可解释性与因果推理的时序分析研究者。
时序链接预测（TLP）模型通常仅基于预测精度评估，但此类评估无法检验模型是否捕捉了时序交互的真实因果机制。本文提出一种反事实验证框架，通过生成具有已知真实因果结构的因果时序交互图（CTIGs）来评估模型。首先，我们引入一种支持激发与抑制效应的连续时间事件序列结构方程模型，并将其扩展至时序交互图。为比较因果模型，提出基于跨模型预测误差的距离度量，并实证验证：在因果机制差异较大的模型上训练的预测器，其性能会显著下降。最后，在（i）生成模型间受控因果转移和（ii）时间戳随机打乱作为可度量因果距离的随机扰动下，实例化反事实评估。该框架为因果感知基准测试提供了基础。
原文摘要 · Abstract (English)
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.
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