Teaching
Lecture: Experimental and Observational Approaches to Causal Inference
This course will teach students how to analyze political science questions through the application of causal inference methods. We will begin by asking what it means for X to cause Y using the potential outcomes framework. We will then look at some of the most popular research designs in causal analysis including experiments, regression discontinuity designs, difference-in-differences / two-way fixed effects, instrumental variables and matching. As illustrations of these methods, we will read some fun papers from different subfields of political science.
The link to the syllabus is here
Tutorial: Applied Causal Inference
This Tutorial is meant as a companion to the Lecture on Experimental and Observational Approaches to Causal Inference. While the Lecture covers the logic underlying experiments and other causal research designs via examples from published research, the Tutorial will provide students with the opportunity to apply these methods themselves using replication datasets. The course will be taught in R. Prior familiarity with R is helpful, but not necessary (although you will have to do some work outside of class to learn the basics).
The link to the syllabus is here
Seminar: Field Experiments
This seminar will provide a collaborative and immersive research experience where students work together with the instructor to design, implement, and analyze a field experiment. Upon completion of this course, students will have first-hand experience with the entire “research process cycle” from study design and pre-analysis planning, through fieldwork and data collection, to statistical analysis and communication of the final results.
The link to the syllabus is here.