“Machine Learning for Epidemiologic and Health Policy Analysis”
CTML faculty Alan Hubbard alongside CTML Rachael Phillips will facilitate a June 24-28 course focusing on advances in machine learning and its application to causal inference and prediction via Targeted Learning, which allows the use of machine learning algorithms for prediction and estimating so-called causal parameters, such as average treatment effects, optimal treatment regimes, etc. We focus on applications in estimating causal impacts of hypothetical healthcare interventions inspired by the Norwegian health data registries.
CTML Feature: Alan Hubbard, Ph.D., Rachael Phillips, Ph.D.