IRIS: An Iterative and Integrated Framework for Real-Time Causal Discovery

Tao Feng, Lizhen Qu, Niket Tandon, Gholamreza Haffari

Keywords: causal discovery, real-time, large language model

TL;DR: We propose a novel framework designed to automatically collect observational data, leverage existing knowledge to enhance causal discovery results, and propose missing variables.

Abstract: Causal discovery is fundamental to scientific research, yet traditional statistical algorithms face significant challenges, including expensive data collection, redundant examination of known relations, and unrealistic assumptions. Additionally, while recent LLM-based methods excel at identifying commonly known causal relations, they fall short in uncovering novel relations. We introduce IRIS (Iterative Retrieval and Integrated System for Real-Time Causal Discovery), a novel framework that addresses these limitations. Starting with a set of initial variables, IRIS automatically retrieves relevant documents, extracts variable values, and organizes data for statistical algorithms in real-time. Our hybrid causal discovery method combines statistical algorithms and LLM-based methods to discover existing and novel causal relations. The missing variable proposal component identifies missing variables, and subsequently, IRIS expands the causal graphs by including both the initial and the newly suggested variables. Our approach offers a scalable and adaptable solution for causal discovery, enabling the exploration of causal relations from a set of initial variables without requiring pre-existing datasets.

Primary Area: causal reasoning

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Submission Number: 6543