Teaching

I will be teaching Causal Inference in the Fall at NYU’s Center for Data Science. This course will provide students with the tools for understanding the relationship between cause and effect. We will discuss why data science needs causal inference, the different frameworks of causal inference (i.e., potential outcomes), and how analysts/researchers may create and use data to infer causality. Topics: Potential Outcomes Framework, Direct Acyclic Graphs, Experiments, quasi- and natural- experiments, Regression Discontinuity, Fixed Effects, Matching, and more.

Supervising Masters of Data Science students in the industry concentration as they pursue internships.