Syllabus
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 practice 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.
Lecture Slides
- Potential Outcomes
- Permutation Tests
- Permutation Inference
- Inference for the Average Treatment Effects
- Stratified Randomized Experiments
- Simple Linear Regression
- Covariate Adjustment in Randomized Experiments
- Noncompliance in Randomized Experiments
- Instrumental Variables
- Regression Discontinuity Designs
- Regression with Observational Data
- Matching Methods
- Weighting Methods
- Differnece-in-Differences Designs
- Causal Directed Acyclic Graphs
- Heterogeneous Treatment Effects
- Partial Identification