Abstract: Jill Furzer's (University of Toronto, Expected 2020) research focuses on combining ensemble learning methods with an economics causal inference tool-kit to predict mental health risk in childhood, assess drivers of marginal misdiagnosis, and understand long-term socioeconomic implications of missed, late or low-value diagnoses. Jill compares classic regression with regularized regression and gradient boosted trees to estimate latent mental health risk in childhood in a nationally representative longitudinal health survey dataset, and further examines how sensitive these models are to protected subgroup information, including gender, rural v. urban, and socioeconomic status. Jill's past research has further focused on modelling the cost-effectiveness of various pediatric oncology screening guidelines and treatments.