CSQL: Mapping Documents into Causal Databases

2026-01-13

AI Summary

This work proposes an end-to-end method to automatically compile unstructured text into a SQL-queryable causal database (CDB) that supports causal algebra queries, thereby enabling answers to “why” questions—addressing the limitation of traditional retrieval-augmented generation (RAG) or knowledge graphs, which only support associative retrieval. Built upon the DEMOCRITUS system, the approach integrates causal discourse analysis, causal statement extraction, and database compilation techniques to construct localized causal models capable of intervention and counterfactual reasoning. The method successfully constructs a CDB from the TCC dataset, comprising 45,319 economics papers and 265,656 causal statements, achieving, for the first time, cross-document identification of causal hubs and longitudinal causal analysis.

Technology Category

• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Machine Learning: Causal Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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.

Problem

• causal database
• unstructured text
• causal query
• document conversion
• causal analysis

Innovation

• causal database
• CSQL
• causal inference
• structured causal queries
• document-to-database compilation