Causal Seminar: Laura Balzer, University of California, Berkeley

Improving community health in East Africa with causal inference and machine learning 

Community-based studies often face the dual challenges of missing data and limited sample sizes. In this talk, we highlight the use of TMLE with Super Learner to reduce bias due to missing outcomes and to improve precision when estimating effects in studies with few independent units and high levels of dependence within those units. We illustrate with the SEARCH Study, a cluster randomized trial for HIV prevention and improved community health in rural Kenya and Uganda. We conclude with some open challenges and proposed solutions in the design and analysis of community-based studies. 

Optional pre-reading: Two-Stage TMLE to reduce bias and improve efficiency in cluster randomized trials