Can Large Language Models Help Experimental Design for Causal Discovery?
Keywords: Large Language Model, Experimental Design, Causal Discovery
TL;DR: We propose a new framework to leverage LLMs for experimental design in online causal discovery.
Abstract: Designing proper experiments and intervening targets is a longstanding problem in scientific or causal discovery. It is fundamentally impossible to identify the underlying causal structure merely based on the observational data. Obtaining interventional data, on the other hand, is crucial to causal discovery, yet it is usually expensive or time-consuming to obtain sufficient interventional data to facilitate causal discovery. Previous approaches usually leverage uncertainty or gradient signals to determine the intervention targets, and may suffer from the suboptimality. In this work, we investigate a different approach, whether we can leverage Large Language Models (LLMs) to assist with the intervention targeting in causal discovery by making use of the rich world knowledge about the experimental design in LLM. Specifically, we present Large Language Model Guided Intervention Targeting (LeGIT), a robust framework that effectively incorporates LLMs to assist with the intervention targeting in causal discovery. Surprisingly, across 4 different scales of realistic benchmarks, LeGIT significantly outperforms previous approaches. LeGIT opens up a new frontier for using LLMs in experimental design.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 7716
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