用专家猜测引导统计检验顺序，有限样本下安全提升因果发现效果
From Guess2Graph: When and How Can Unreliable Experts Safely Boost Causal Discovery in Finite Samples?
- 专家猜测不直接替代检验，而是指导测试顺序，保持统计正确性
- 专家越准，效果越好，且在有限样本中可证明优于无专家版本
- 适合需融合领域知识的因果推断场景，尤其适用于低样本数据
因果发现算法在样本有限时表现不佳。虽然将专家知识（包括来自大语言模型的）作为约束可提升性能，但现有方法要求专家预测完全准确或具备不确定性估计，实用性受限。本文提出Guess2Graph（G2G）框架，利用专家猜测引导统计检验序列，而非取代检验过程，从而在保持统计一致性的同时实现性能提升。我们开发了两种实例：PC-Guess，增强经典PC算法；gPC-Guess，一种学习增强型变体，更高效利用高质量专家输入。理论上，两者均对专家错误具有鲁棒性，且当专家表现优于随机猜测时，gPC-Guess在有限样本下可严格优于其非增强版本。实验表明，两者均随专家准确性单调提升，且gPC-Guess取得显著更强增益。
原文摘要 · Abstract (English)
Causal discovery algorithms often perform poorly with limited samples. While integrating expert knowledge (including from LLMs) as constraints promises to improve performance, guarantees for existing methods require perfect predictions or uncertainty estimates, making them unreliable for practical use. We propose the Guess2Graph (G2G) framework, which uses expert guesses to guide the sequence of statistical tests rather than replacing them. This maintains statistical consistency while enabling performance improvements. We develop two instantiations of G2G: PC-Guess, which augments the PC algorithm, and gPC-Guess, a learning-augmented variant designed to better leverage high-quality expert input. Theoretically, both preserve correctness regardless of expert error, with gPC-Guess provably outperforming its non-augmented counterpart in finite samples when experts are "better than random." Empirically, both show monotonic improvement with expert accuracy, with gPC-Guess achieving significantly stronger gains.
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