Revealing hidden causal variables alongside the underlying causal mechanisms is essential to the development of science. Despite the progress in the past decades, existing practice in causal discovery (CD) heavily relies on high-quality measured variables, which are usually given by human experts. In fact, the lack of well-defined high-level variables behind unstructured data has been a longstanding roadblock to a broader real-world application of CD. This procedure can naturally benefit from an automated process that can suggest potential hidden variables in the system. Interestingly, Large language models (LLMs) are trained on massive observations of the world and have demonstrated great capability in processing unstructured data. To leverage the power of LLMs, we develop a new framework termed Causal representatiOn AssistanT (COAT) that incorporates the rich world knowledge of LLMs to propose useful measured variables for CD with respect to high-value target variables on their paired unstructured data. Instead of directly inferring causality with LLMs, COAT constructs feedback from intermediate CD results to LLMs to refine the proposed variables. Given the target variable and the paired unstructured data, we first develop COAT-MB that leverages the predictivity of the proposed variables to iteratively uncover the Markov Blanket of the target variable. Built upon COAT-MB, COAT-PAG further extends to uncover a more complete causal graph, i.e., Partial Ancestral Graph, by iterating over the target variables and actively seeking new high-level variables. Moreover, the reliable CD capabilities of COAT also extend the debiased causal inference to unstructured data by discovering an adjustment set. We establish theoretical guarantees for the CD results and verify their efficiency and reliability across realistic benchmarks and real-world case studies.

翻译：揭示隐藏的因果变量及其背后的因果机制对于科学发展至关重要。尽管过去几十年取得了进展，但现有的因果发现实践严重依赖于高质量测量变量，而这些变量通常由人类专家提供。事实上，非结构化数据背后缺乏明确定义的高层变量，一直是阻碍因果发现在更广泛现实世界中应用的一个长期障碍。这一过程自然可以从能够提出系统中潜在隐藏变量的自动化流程中受益。有趣的是，大语言模型基于对世界的海量观察进行训练，并在处理非结构化数据方面展现出强大能力。为利用大语言模型的能力，我们开发了一个名为因果表征助手的新框架，该框架结合大语言模型丰富的世界知识，针对高价值目标变量及其配对非结构化数据，提出用于因果发现的有效测量变量。与直接使用大语言模型推断因果关系不同，因果表征助手通过构建从中间因果发现结果到大语言模型的反馈来优化所提出的变量。给定目标变量及其配对非结构化数据，我们首先开发了因果表征助手-马尔可夫毯，它利用所提变量的预测性来迭代揭示目标变量的马尔可夫毯。在此基础上，因果表征助手-部分祖先图进一步扩展，通过遍历目标变量并主动寻求新的高层变量，揭示更完整的因果图，即部分祖先图。此外，因果表征助手可靠的因果发现能力还通过发现调整集，将无偏因果推断扩展至非结构化数据。我们为因果发现结果建立了理论保证，并在实际基准测试和真实案例研究中验证了其效率和可靠性。