Substantive questions in empirical scientific and policy research
are often causal. Does voter outreach increase turnout? Are job
training programs effective? Can a universal health insurance
program improve people's health? This class will introduce students
to both statistical theory and applications of causal inference. As
theoretical frameworks, we will discuss potential outcomes, causal
graphs, randomization and model-based inference, sensitivity
analysis, and partial identification. We will also cover various
methodological tools including randomized experiments, regression
discontinuity designs, matching, regression, instrumental variables,
difference-in-differences, and dynamic causal models. The course
will draw upon examples from political science, economics,
education, public health, and other disciplines.
This course was listed as Stat186/Gov2002 in 2018 and 2019.