SMMAL: How Semi-Supervised Machine Learning Finally Solves Treatment Effect Estimation from Messy Health Records
A research team from the University of Minnesota and Harvard built a semi-supervised estimator that handles the most realistic and frustrating scenario in real-world health data — when both the treatment a patient received and the health outcome that followed…