Causal Inference and Measurement for Data Science

Catalog description

Principles and practice of causal inference and measurement for data scientists. Topics include: fundamentals of causal inference, establishing causal (rather than correlational) relationships -- via AB tests, controlling for confounders, and the use of panel data -- and the fundamentals of measurement. Validity and reproducibility are themes throughout the course.