Predicting Causal Effects from Natural Language Queries using Structured Representations 事件

Predicting Causal Effects from Natural Language Queries using Structured Representations arXiv:2605.29631v1 Announce Type: new Abstract: Randomized controlled trials are a cornerstone of medicine and the social sciences as they enable reliable estimates of causal effects. However, they are costly and time-consuming to conduct, motivating interest in predicting causal effects from existing experimental evidence. Recent advances in large language models (LLMs) have demonstrated strong performance