Test Time Training for Supervised Causal Learning
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866910270215946240 |
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| author | Deng, Zizhen Zhang, Jiaru Ding, Rui Bojun, Huang Wang, Jinzhuo Fu, Qiang Han, Shi Zhang, Dongmei |
| author_facet | Deng, Zizhen Zhang, Jiaru Ding, Rui Bojun, Huang Wang, Jinzhuo Fu, Qiang Han, Shi Zhang, Dongmei |
| contents | 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 novel framework that dynamically generates training sets explicitly aligned with any specific test instance. 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 benchmarks, pseudo-real and real-world datasets demonstrate that TTT-SCL significantly outperforms existing SCL and traditional causal discovery methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_30015 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Test Time Training for Supervised Causal Learning Deng, Zizhen Zhang, Jiaru Ding, Rui Bojun, Huang Wang, Jinzhuo Fu, Qiang Han, Shi Zhang, Dongmei Machine Learning Artificial Intelligence 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 novel framework that dynamically generates training sets explicitly aligned with any specific test instance. 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 benchmarks, pseudo-real and real-world datasets demonstrate that TTT-SCL significantly outperforms existing SCL and traditional causal discovery methods. |
| title | Test Time Training for Supervised Causal Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2605.30015 |