用解耦机制提升因果发现模型在复杂数据中的零样本泛化能力
DCD-PFN: A Decoupling-Aware Foundation Model for Causal Discovery
- 基于解耦思想，通过预训练学习样本级解耦权重
- 在多种合成因果模型上实现稳定零样本因果图重构
- 适合需要快速推断因果结构的研究者和工程师
因果发现对理解复杂数据生成机制至关重要，但传统方法在高度非线性和噪声系统中表现不佳，或存在严重计算瓶颈。基于先验-数据拟合网络（PFNs）的表格基础模型虽展现出出色的零样本推理能力，但在显式结构因果发现方面的潜力尚未被充分挖掘。为此，我们提出DCD-PFN，一种面向因果发现的解耦感知基础模型。该模型不直接进行全局图重建，而是采用基于解耦的局部因果发现范式。通过在多样化合成结构性因果模型（SCMs）上预训练，模型学习样本级解耦权重，从而实现马尔可夫边界（MB）识别。此外，借助并行化的局部发现机制，DCD-PFN能高效重构全局因果图，同时保持解耦因果发现的理论根基。实验表明，该基础模型实现了稳健的零样本泛化性能。
原文摘要 · Abstract (English)
Causal discovery is critical for understanding complex data-generating mechanisms, yet traditional algorithms often struggle with highly non-linear and noisy systems, or suffer from severe computational bottlenecks. Recent tabular foundation models based on Prior-Data Fitted Networks (PFNs) have demonstrated remarkable zero-shot inference capabilities, but their potential for explicit structural causal discovery remains underexplored. To bridge this gap, we propose DCD-PFN, a decoupling-aware foundation model for causal discovery. Instead of directly amortizing global graph reconstruction, DCD-PFN focuses on local causal discovery through a decoupling-based paradigm. Through pre-training on diverse synthetic Structural Causal Models (SCMs), the model learns sample-wise decoupling weights that enable Markov boundary (MB) identification. Furthermore, by leveraging parallelized local discovery, DCD-PFN efficiently reconstructs global causal graphs while remaining grounded in the theoretical foundations of decoupling-based causal discovery. Experiments demonstrate that our foundation model achieves robust zero-shot generalization.
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