Discovering and Reasoning of Causality in the Hidden World with Large Language Models
借助大语言模型发现隐含变量并开展因果发现与推理AI 解读 ↓
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借助大语言模型发现隐含变量并开展因果发现与推理
- 研究问题
- 当非结构化数据缺乏专家预先定义的高层变量时，如何借助大语言模型提出可测量变量，并逐步发现目标相关的因果结构？
- 方法与思路
- 作者提出COAT框架，以中间因果发现结果反馈修正语言模型生成的变量，先探索马尔可夫毯，再扩展到部分祖先图和调整集发现。
- 材料陈述的贡献
- 摘要称框架为因果发现结果提供理论保证，并在基准与案例中验证效率和可靠性，同时将调整集发现用于非结构化数据的因果推断。
- 阅读关注点
- 应核对变量生成与测量过程、反馈终止规则及理论保证条件，尤其检查调整集发现如何处理潜在混杂和语言模型生成变量的误差。
- 阅读边界
- 这是arXiv预印本，不能据此确认同行评审或期刊接收；摘要未说明理论假设与具体验证指标，不能据此认定语言模型能够直接识别因果关系。
原文摘要
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.