Evan Munro’s research interests are in econometrics and causal inference, with a focus on designing experiments, estimating treatment effects, and formulating optimal policy in markets and other strategic environments. His work combines tools from machine learning, statistics and game theory. Specific areas of interest include online platforms, environmental and resource markets, and matching markets for schools.  His research has been published in Management Science and Journal of Econometrics. 

Munro earned a PhD in economics from Stanford University.  He also holds an MPhil in economics with distinction from Nuffield College, University of Oxford and a BA in economics and computer science-mathematics from Columbia University. 

Prior to joining Booth, Munro was Neyman Visiting Assistant Professor of Statistics at University of California, Berkeley.