Causal inference for time-varying exposures: g-formula, marginal structural models, g-estimation of structural nested models; static and dynamic treatment regimes.
Structural causal models, graphical models, algorithms for identification.
Developed and taught short course to introduce R to graduate students. Topics included creating figures and tables, cleaning data, writing functions.
Epidemiology course for participants in the Harvard Summer Program in Biostatistics & Computational Biology.
Regression models, sampling, longitudinal and multilevel analysis, time-varying confounding, mediation and interaction, econometric methods, missing data.
Basic epidemiologic methods, overview of epidemiology of diseases/conditions of public health importance for undergraduates.
Study design, sampling, data collection, analysis, inference for MPH students.