CDFM：迈向通用因果发现基础模型

中文摘要 AI 辅助

因果发现是从观测数据中恢复潜在因果结构的过程，在多学科领域至关重要。过去几十年，针对特定数据集机制开发了众多算法，但面对数据增长，特定数据集方法导致碎片化、测试驱动范式难以满足现代科学发现需求。为此，提出因果发现基础模型（CDFM）作为零样本结构推理的统一通用框架。先研究因果可识别性理论边界，揭示因果先验机制作用，在此基础上构建变分框架，将未知因果机制视为潜在变量，分解边际似然。通过在大量多样的合成结构因果模型上预训练，CDFM成功内化复杂统计不对称性。大量实验表明，CDFM性能优于传统算法，推动向通用因果发现基础模型的范式转变。

英文摘要

Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines. Over the past decades, numerous algorithms have been developed to tackle this challenge through workflows tailored to the specific causal mechanisms underlying each type of dataset, demonstrating effectiveness across a wide range of applications. However, as the volume and heterogeneity of real-world data continue to grow, this dataset-specific approach inevitably leads to a fragmented, test-driven paradigm that struggles to scale to the demands of modern scientific discovery. To address this, we formulate the Causal Discovery Foundation Model (CDFM) as a unified, general-purpose framework for zero-shot structural inference. To ensure reliable generalization across unknown domains, we first investigate the theoretical boundaries of causal identifiability, revealing the indispensable role of causal prior mechanisms in this process. Building on these insights, we formulate a principled variational framework that treats unknown causal mechanisms as latent variables and mathematically decomposes the intractable marginal likelihood into distinct, tractable learning modules. The variational decomposition provides a conceptual design principle for the architecture design of CDFM, while comprehensive causal knowledge guides the large-scale synthesis of our pretraining data. By pretraining on a massive, highly diverse space of synthetic structural causal models, CDFM successfully internalizes complex statistical asymmetries. Extensive experiments demonstrate that CDFM consistently outperforms traditional algorithms, driving a paradigm shift toward a general-purpose causal discovery foundation model.

发表机构

• School of Computer Science, Guangdong University of Technology, Guangzhou, China（广东技术大学计算机科学学院）
• College of Mathematics and Computer, Shantou University, Shantou, China（汕头大学数学与计算机学院）
• Department of Computer Science and Technology, Tsinghua University, Beijing, China（清华大学计算机科学与技术系）

机构由 AI 辅助整理，请以论文原文为准。