We describe a novel system, CSQL, which automatically converts a collection of unstructured text documents into an SQL-queryable causal database (CDB). A CDB differs from a traditional DB: it is designed to answer "why'' questions via causal interventions and structured causal queries. CSQL builds on our earlier system, DEMOCRITUS, which converts documents into thousands of local causal models derived from causal discourse. Unlike RAG-based systems or knowledge-graph based approaches, CSQL supports causal analysis over document collections rather than purely associative retrieval. For example, given an article on the origins of human bipedal walking, CSQL enables queries such as: "What are the strongest causal influences on bipedalism?'' or "Which variables act as causal hubs with the largest downstream influence?'' Beyond single-document case studies, we show that CSQL can also ingest RAG/IE-compiled causal corpora at scale by compiling the Testing Causal Claims (TCC) dataset of economics papers into a causal database containing 265,656 claim instances spanning 45,319 papers, 44 years, and 1,575 reported method strings, thereby enabling corpus-level causal queries and longitudinal analyses in CSQL. Viewed abstractly, CSQL functions as a compiler from unstructured documents into a causal database equipped with a principled algebra of queries, and can be applied broadly across many domains ranging from business, humanities, and science.

翻译：本文介绍了一种新颖的系统CSQL，该系统能够自动将非结构化文本文档集合转换为支持SQL查询的因果数据库。因果数据库与传统数据库不同：其设计目标是通过因果干预和结构化因果查询来回答“为什么”类问题。CSQL基于我们早期开发的DEMOCRITUS系统构建，后者能够将文档转化为数千个从因果论述中推导出的局部因果模型。与基于检索增强生成（RAG）的系统或基于知识图谱的方法不同，CSQL支持对文档集合进行因果分析，而非纯粹的关联检索。例如，给定一篇关于人类双足行走起源的文章，CSQL能够支持诸如“影响双足行走的最强因果因素是什么？”或“哪些变量是下游影响最大的因果枢纽？”等查询。除单文档案例研究外，我们进一步证明CSQL能够大规模处理由RAG/信息抽取技术编译的因果语料库：通过将经济学论文数据集“Testing Causal Claims”编译为包含265,656个主张实例的因果数据库，这些实例涵盖45,319篇论文、44年时间跨度和1,575条报告方法字符串，从而在CSQL中实现语料库级别的因果查询与纵向分析。从抽象视角看，CSQL可视为将非结构化文档编译为具备规范化查询代数的因果数据库的编译器，其应用可广泛覆盖商业、人文与科学等诸多领域。