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Quantitative Causal Inference (406-0-20)

Instructors

Jaye Seawright
847/467-1148
Office Hours: http://www.polisci.northwestern.edu/people/core-faculty/jason-seawright.html

Meeting Info

Scott Hall 212: Tues, Thurs 12:30PM - 1:50PM

Overview of class

This course offers an introduction to quantitative approaches to
causal inference in the social sciences.

The goals of the course involve starting a lifetime of engagement with the rapidly evolving literature behind applied quantitative causal inference. While causal inference is difficult and far from straightforward, even in most experiments, scholars and practitioners have developed and continue to produce clever and insightful ideas that help us design studies and analyze results in ways that are more coherent, insightful, and reliable. But because these ideas are both exciting and important, new approaches are constantly emerging, and that state of affairs is likely to continue! We need to become good not just at a set of techniques but also at picking up new approaches.

We will consider difference-in-differences designs, matching methods, instrumental variables, regression-discontinuity designs, and more. We will look at classic contributions, new ideas in each field, and the tools and insights that we need to move across areas and pick up different techniques.

Registration Requirements

Graduate students only

Learning Objectives

By the end of this seminar, students will be able to:

  • Translate between mathematical and verbal descriptions of causal inference estimators;
  • Use a published article and its affiliated R package to implement and correctly interpret a causal inference estimator with data;
  • Evaluate a collection of causal inference estimators related to a single research design in order to either select a best estimator for the research context of interest or to conclude that the estimators are interchangeable given current knowledge;
  • Correctly describe, and when feasible, test the assumptions involved with each family of causal inference research designs;
  • Communicate about quantitative causal inference at a professional level in a way appropriate for workshop, conference, and other relevant conversational settings;
  • Produce a written "grant proposal" that features a research design that shows mastery of at least one cutting-edge quantitative causal inference estimator.

Teaching Method

Seminar

Enrollment Requirements

Enrollment Requirements: REASON: Pre-registration is not allowed for this class. Please try again during regular registration. Reserved for Graduate Students.