Predicting Causal Effects from Natural Language Queries using Structured Representations

1. 1Build a dataset of natural language queries related to causal effects
2. 2Run large language models on the dataset to generate structured representations
3. 3Configure the models to predict causal effects from the representations
4. 4Test the performance of the models using evaluation metrics
5. 5Apply the predicted causal effects to real-world problems in medicine and social sciences

Data scientists and researchers on a team can benefit from this approach to predict causal effects, and software engineers can help implement the solution using large language models

💡 Large language models can be used to predict causal effects from natural language queries, enabling faster and more cost-effective estimates of causal effects

💡 Predict causal effects from natural language queries using LLMs!