About
Bernardo Modenesi is a Ph.D. candidate in Economics, also pursuing a Master’s degree in Statistics, at the University of Michigan. Within the university, Bernardo organizes the Complex Systems Advanced Academic Workshop. Outside the university, he participates as a research fellow at the Institute for Applied Economic Research (IPEA) in Brazil. Bernardo's interests lie in interdisciplinary statistical methods, such as network theory and machine learning, that can be used for the improvement of causal inference exercises and economic modelling. Recently, Bernardo has been working on using network theory and discrete choice models to leverage information contained in linked employee-employer data for labor market modeling and also for enhancing counterfactual analysis. His past research also includes the partial identification of marginal treatment effects under the monotonicity assumption. Prior to beginning his Ph.D., Bernardo earned a Master’s degree in Economics from the Sao Paulo School of Economics (FGV-SP), where he also worked as a research assistant on econometric theory papers, which were published in top journals. Bernardo also has previous experience working with applied econometrics in the think tank Centers for Learning on Evaluation and Results (CLEAR-FGV).
