Causal Rationales in Graphs: Explaining OOD Generalization Failures via Environment-Dependent Rationales

Building on the causal perspective in Wu et al. (2024), Xu et al. (2024), and the mutable rationale invariance explored by Xu et al. (2024, DEROG), this idea proposes to operationalize “rationales” as minimal subgraphs or patterns that drive a prediction. By tracking how these causal rationales change across environments (i.e., different node/edge distributions), researchers can directly tie model failures to rationale drift, rather than only to feature or label shift. This would involve developing new algorithms for rationale extraction in GNNs and new metrics for rationale (in)variance. The novelty lies in making the explanations themselves the unit of OOD analysis, rather than just performance metrics or internal representations. Such a tool could diagnose OOD failures at a fine-grained, interpretable level and inform new interventions (such as rationale-based adversarial training or rationale consistency regularization).

References:

1. Graph Out-of-Distribution Generalization via Causal Intervention. Qitian Wu, Fan Nie, Chenxiao Yang, Tianyi Bao, Junchi Yan (2024). The Web Conference.
2. Improving Graph Out-of-distribution Generalization Beyond Causality. Can Xu, Yao Cheng, Jianxiang Yu, Haosen Wang, Jingsong Lv, Yao Liu, Xiang Li (2024).
3. Improving Graph Out-of-distribution Generalization on Real-world Data. Can Xu, Yao Cheng, Jianxiang Yu, Haosen Wang, Jingsong Lv, Xiang Li (2024). arXiv.org.