I work at the intersection of economic theory and applied data analysis: finding what's actually driving an outcome, and being precise about what the data can and can't say about it. My background is in causal inference, econometrics, and machine learning, applied to questions in energy markets, urban transit, and labor markets. The toolkit includes difference-in-differences, instrumental variables, fixed effects, causal graphs, and gradient-boosted models. This site collects that work: finished projects, work in progress, and the reasoning behind both.
Causal analysis of ridership, fare equity, and transit strain across six 2026 World Cup host cities. Synthetic control, panel DiD, event studies, and an interactive D3/Leaflet dashboard. Living analysis updated weekly.
Hedonic OLS on 8,778 Montreal Airbnb listings shows the naive transit premium reverses sign once centrality enters: rail proximity is a centrality effect in disguise, and conditional on location, rail adjacency carries a slight price discount.
Economist. Previously at CIGI, Ontario Ministry of Health, and the Department of Economics, University of Waterloo. BA Mathematics and Economics, McGill University.