Causal Machine Learning in Practice: Estimating Average and Heterogeneous Effects for Personalized Treatment.

A "brown-bag" talk that builds from classical econometrics intuition (Frisch-Waugh-Lovell, potential outcomes) through modern semi-parametric methods (Double/Debiased ML, meta-learners) and CATE estimation techniques, and landing on practical marketing applications like audience selection and coupon targeting. The goal is to make these methods feel like a natural extension of what an econometrics-trained student already knows.

High, But Not Happy? The Impact of Cannabis Consumption on Mental Health

An econometric analysis examining the causal relationship between cannabis consumption and mental health outcomes using advanced econometric techniques and longitudinal data analysis.

Particule Happiness: How Air Pollution is Effecting Our Mental Health

A comprehensive study investigating the impact of air pollution on mental health outcomes using econometric modeling and environmental data analysis with geospatial techniques.