Causal Temporal Graphs for Counterfactual Validation of Temporal Link Prediction

Aniq Ur Rahman, Justin Coon
Proceedings of The 1st Symposium on Probabilistic Machine Learning, PMLR 327:119-134, 2026.

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 divergence 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 divergence. Our framework provides a foundation for causality-aware benchmarking.

Cite this Paper

BibTeX
@InProceedings{pmlr-v327-rahman26a, title = {Causal Temporal Graphs for Counterfactual Validation of Temporal Link Prediction}, author = {Rahman, Aniq Ur and Coon, Justin}, booktitle = {Proceedings of The 1st Symposium on Probabilistic Machine Learning}, pages = {119--134}, year = {2026}, editor = {Swaroop, Siddharth and Rügamer, David and Kristiadi, Agustinus}, volume = {327}, series = {Proceedings of Machine Learning Research}, month = {05 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v327/main/assets/rahman26a/rahman26a.pdf}, url = {https://proceedings.mlr.press/v327/rahman26a.html}, 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 divergence 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 divergence. Our framework provides a foundation for causality-aware benchmarking. } }
Endnote
%0 Conference Paper %T Causal Temporal Graphs for Counterfactual Validation of Temporal Link Prediction %A Aniq Ur Rahman %A Justin Coon %B Proceedings of The 1st Symposium on Probabilistic Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Siddharth Swaroop %E David Rügamer %E Agustinus Kristiadi %F pmlr-v327-rahman26a %I PMLR %P 119--134 %U https://proceedings.mlr.press/v327/rahman26a.html %V 327 %X 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 divergence 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 divergence. Our framework provides a foundation for causality-aware benchmarking.
APA
Rahman, A.U. & Coon, J.. (2026). Causal Temporal Graphs for Counterfactual Validation of Temporal Link Prediction. Proceedings of The 1st Symposium on Probabilistic Machine Learning, in Proceedings of Machine Learning Research 327:119-134 Available from https://proceedings.mlr.press/v327/rahman26a.html.

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