Collaborative Research: MFAI: Two Sides of a Tapestry: Causal Inference and Model Discovery Amid Information Gaps in Complex Data

This project aims to advance machine learning methods for discovering cause-and-effect relationships in complex systems. While much of modern data science focuses on identifying patterns and correlations in data, such associations cannot explain why events happen or how changing one factor might influence another. Causal discovery addresses this…