Laura Balzer

Laura Balzer, PhD, MPhil is an Associate Professor of Biostatistics at the University of California, Berkeley. Her research focuses on methodological and applied work in causal inference, machine learning, and messy real-world data, particularly in the context of HIV prevention and global health in East Africa.

• PhD – Biostatistics, University of California, Berkeley (2015)
• MPhil – Computational Biology, University of Cambridge (2009)
• BS – Applied Mathematics, University of Vermont (2008)

Dr. Balzer specializes in the design and analysis of cluster randomized and pragmatic trials, addressing challenges like differential measurement, complex dependence, and missing data. Her work integrates epidemiologic methods with machine learning to enhance rigor in real-world studies. Recent publications emphasize community-based HIV interventions, dynamic choice models, and causal inference frameworks for global health applications in Kenya and Uganda.

Her methodological contributions include Two-Stage TMLE for handling sub-sampling and non-independent units, while applied studies examine HIV-tuberculosis interactions, hypertension care models, and social network effects on health outcomes. Dr. Balzer’s role as a Primary Statistician for East African studies underscores her commitment to translating academic advances into public health impact.