Project in Prairie
• Disciplinary research: She will continue to pursue research in health economics, taking advantage
of the incredible opportunity to use health claims data from the National Health Data System (SNDS). This data can now be linked with administrative registers with socio-economic info at the individual level (EDP-Santé (3.4 million individuals, 2008-2022)). She will pursue her work on the interplay between insecure careers and health over the life-course, combining causal inference methods with Machine Learning (ML) techniques and natural experiments. In particular, she will study the health spillovers of mass layoffs, the matching of patients to doctors, and the role of prescription style on health and labor-market trajectories.
• Interdisciplinary research: Despite its initial focus on causal inference, there’s a growing
interest in economics for ML and its applications (collect new data, prediction for policy, ML for
econometrics). Conversely, there’s a clear interest in ML for causal inference. At the intersection of
the two disciplines, there is a fast-growing literature on high-dimensionality and endogeneity, or on
the estimation of heterogeneous treatment effects for optimal resource allocation. Within PR[AI]RIE,
she is eager to collaborate with mathematicians or computer scientists with an interest in causal inference methods.