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arXiv · 2603.04276

Causality Elicitation from Large Language Models

Takashi Kameyama·Masahiro Kato·Yasuko Hio·Yasushi Takano·Naoto Minakawa
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Abstract

Large language models (LLMs) are trained on enormous amounts of data and encode knowledge in their parameters. We propose a pipeline to elicit causal relationships from LLMs. Specifically, (i) we sample many documents from LLMs on a given topic, (ii) we extract an event list from from each document, (iii) we group events that appear across documents into canonical events, (iv) we construct a binary indicator vector for each document over canonical events, and (v) we estimate candidate causal graphs using causal discovery methods. Our approach does not guarantee real-world causality. Rather, it provides a framework for presenting the set of causal hypotheses that LLMs can plausibly assume, as an inspectable set of variables and candidate graphs.

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Takashi Kameyama, Masahiro Kato, Yasuko Hio, Yasushi Takano, Naoto Minakawa. 2026-03-04. Causality Elicitation from Large Language Models. https://arxiv.org/abs/2603.04276

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