Dharmesh Dhabliya

Causal Machine Learning for Discovering Actionable Insights in Observational Data

A unified causal machine learning framework that integrates structural causal modeling with data-driven estimation techniques to enable robust causal discovery and effect estimation and achieves higher causal discovery accuracy with improved precision and recall of causal edges.