Can Large Language Models Help Experimental Design for Causal Discovery?

Understand

Designing proper experiments and selecting optimal intervention targets is a longstanding problem in scientific or causal discovery.

• Identifying the underlying causal structure from observational data alone is inherently difficult.
• Obtaining interventional data, on the other hand, is crucial to causal discovery, yet it is usually expensive and time-consuming to gather sufficient interventional data to facilitate causal discovery.
• Previous approaches commonly utilize uncertainty or gradient signals to determine the intervention targets.