Test Time Training for Supervised Causal Learning

Supervised Causal Learning (SCL) has shown promise in causal discovery by framing it as a supervised learning problem. However, it suffers from significant out-of-distribution generalization challenges. We reveal three limitations of previous SCL practices: a significant performance gap between synthetic benchmarks and real-world data, fragility to distribution shifts, and failure in compositional generalization, collectively questioning its real-world applicability. To address this, we propose Test-Time Training for Supervised Causal Learning (TTT-SCL), a framework that dynamically generates training sets aligned with the current test dataset, on which a new supervised learner is trained from random initialization. We demonstrate the correlation between TTT-SCL and score-based methods, and design an efficient module for generating training sets based on the classic scoring function. Experiments on synthetic, pseudo-real, and real-world datasets demonstrate competitive performance and improvements over the compared baselines in challenging settings.