Causal Discovery with Language Models as Imperfect Experts

• URL: http://arxiv.org/abs/2307.02390v1
• Date: Wed, 5 Jul 2023 16:01:38 GMT
• Title: Causal Discovery with Language Models as Imperfect Experts
• Authors: Stephanie Long, Alexandre Pich\'e, Valentina Zantedeschi, Tibor Schuster, Alexandre Drouin
• Abstract summary: We consider how expert knowledge can be used to improve the data-driven identification of causal graphs. We propose strategies for amending such expert knowledge based on consistency properties. We report a case study, on real data, where a large language model is used as an imperfect expert.
• Score: 119.22928856942292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding the causal relationships that underlie a system is a fundamental prerequisite to accurate decision-making. In this work, we explore how expert knowledge can be used to improve the data-driven identification of causal graphs, beyond Markov equivalence classes. In doing so, we consider a setting where we can query an expert about the orientation of causal relationships between variables, but where the expert may provide erroneous information. We propose strategies for amending such expert knowledge based on consistency properties, e.g., acyclicity and conditional independencies in the equivalence class. We then report a case study, on real data, where a large language model is used as an imperfect expert.

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