Michela Bia is a Research Fellow at the Luxembourg Institute of Socio-Economic Research, working at the intersection of causal inference, program evaluation, and labor economics. She is also affiliated with the University of Luxembourg, where she teaches econometrics, data analysis, and machine learning methods.
She obtained her PhD in Applied Statistics from the University of Florence in 2007. She was a Visiting Student Researcher at the University of California, Berkeley, and more recently a Visiting Scholar at the Miami Herbert Business School, University of Miami, collaborating on research in applied econometrics and environmental economics.
Her research focuses on the development and application of microeconometric methods for policy evaluation, with particular emphasis on causal inference, machine learning, and the analysis of labor market and public policy interventions. She has extensive experience working with administrative and high-dimensional data in both methodological and applied economic contexts.
She is the PI of the FNR CORE project SYNERGIX (2026–2029), which investigates firm transformation driven by green and digital innovations, contributing to the understanding of structural change in modern economies. Her previous work includes leading and contributing to large-scale projects on labor market policies, childcare systems, and parental leave, in collaboration with national and European institutions.
She supervises PhD research in applied econometrics and policy evaluation and plays an active role in the international econometrics community, regularly organizing international conferences and workshops with leading scholars. Her work has been published in leading journals including the Journal of Business and Economic Statistics, Journal of the Royal Statistical Society: Series A, Econometric Reviews and The Stata Journal among others. She is a member of the Royal Statistical Society.
Her main research interests include Causal Inference, Machine Learning Artificial Intelligence, Program Evaluation, Business Analytics, Healthcare Systems, Nonparametric methods.
