针对因果学习泛化差的问题，提出测试时动态生成训练数据的新方法。
Test Time Training for Supervised Causal Learning
- 测试时根据具体输入动态构建匹配的训练集
- 在真实数据上性能超越现有方法，显著提升泛化能力
- 适合需要强泛化性的实际因果推断场景
监督因果学习（SCL）通过将因果发现建模为监督学习问题展现出潜力，但面临严重的分布外泛化挑战。我们揭示了以往SCL实践的三大局限：合成基准与真实数据间存在显著性能差距、对分布偏移敏感、在组合泛化任务中失败，共同质疑其实际应用价值。为此，我们提出测试时训练的监督因果学习（TTT-SCL），一种新框架，能针对任意测试实例动态生成与之对齐的训练集。我们证明了TTT-SCL与基于得分的方法之间的关联，并设计了一个基于经典评分函数的高效训练集生成模块。在合成基准、伪真实和真实世界数据集上的实验表明，TTT-SCL显著优于现有的SCL及传统因果发现方法。
原文摘要 · Abstract (English)
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.
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