Avi Feller

Avi Feller is an Associate Professor at the Goldman School of Public Policy and holds a joint appointment in Statistics at the University of California, Berkeley. His research operates at the intersection of public policy, data science, and statistics, focusing on developing methodological approaches to improve social policy evaluations and collaborating with governments to design and implement data-driven policies.

Education:

• Ph.D. in Statistics, Harvard University
• M.Sc. in Applied Statistics, University of Oxford (as Rhodes Scholar)
• B.A. in Political Science and Applied Mathematics, Yale University

Research focuses on quantitative methods for policy evaluation, including causal inference techniques, synthetic control methods, and balancing weights for observational studies. His work spans education, public health, and social policy domains, with particular emphasis on developing robust statistical approaches for real-world policy analysis. Methodological innovations include the Augmented Synthetic Control Method and applications of machine learning to causal inference problems.

Publications demonstrate consistent focus on causal inference methodology with applications to policy-relevant domains including education, gun violence prevention, and COVID-19 interventions. Recent work shows increasing emphasis on machine learning integration and complex observational study designs with temporal and spatial components.

Awards and Honors:

• Rhodes Scholarship

Prior professional experience includes serving as Special Assistant to the Director at the White House Office of Management and Budget and working at the Center on Budget and Policy Priorities. No information is available regarding current graduate students, research grants, or laboratory affiliations.