Discovering and Reasoning of Causality in the Hidden World with Large Language Models

Understand

Revealing hidden causal variables alongside the underlying causal mechanisms is essential to the development of science.

• Despite the progress in the past decades, existing practice in causal discovery (CD) heavily relies on high-quality measured variables, which are usually given by human experts.
• In fact, the lack of well-defined high-level variables behind unstructured data has been a longstanding roadblock to a broader real-world application of CD.
• This procedure can naturally benefit from an automated process that can suggest potential hidden variables in the system.