Sean Tanner has expertise in the economics of education, education policy, and methods of causal inference. His substantial research portfolio focuses on the causes and consequences of inequality in children’s human capital and its relation to lifetime socioeconomic status, with a particular focus on education policy’s ability to intervene and shape this relationship.
Current and recent projects include analyses of the impact of California’s school finance reform on achievement and attainment, the medium-term impact of public prekindergarten, and the impact of fiscal autonomy for schools on equity and efficiency. His methodological research focuses on causal inference in education and policy research, specifically the relative merits and weaknesses of field and natural experiments, statistical analysis, and meta-analytic methods to generate durable scientific and policy knowledge. Current and recent projects include a meta-analysis of multi-trial interventions and an analysis of the power of machine learning algorithms to predict attrition.
He received a PhD in Public Policy from the University of California, Berkeley.