Skip to main content

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: Thurs 2:00PM - 4:50PM

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.

Learning Objectives

Through this seminar, students will practice a set of skills that prepare them for the future, as well as for knowledge of the current state of causal inference. 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

Student-led presentations, lecture, discussion, lab work

Evaluation Method

There are three major categories of assessments for this seminar.

The first involves presenting an estimator in class. Presenting an estimator will involve:

  • Explaining the problem the estimator is intended to address
  • Discussing the equation or equations that instantiate the estimator, interpreting them to the audience
  • Describing the assumptions needed for the estimator, and ideally relating those to the equation(s)
  • Spelling out the strengths and weakness of the estimator in terms of statistical properties like bias, consistency, variance, mean squared error, etc.
  • Showing an applied example of the estimator, either one from published work or an original application


  • An in-class presentation should be ten to twelve minutes of prepared content, and the presenter should be ready for a period of eight to ten minutes of audience questions as the end. The presentation should have professional slides that help illustrate the key ideas --- it is very challenging to discuss statistical estimators, coding, and results without visuals! The goal with all of this is to simulate the experience of a professional conference presentation discussing an idea in methodology, but using a fully attributed discussion of someone else's published ideas as a classroom analogue.


Please meet with J. Seawright as soon as possible (in office hours, by email, etc.) to choose an estimator to present and to plan your presentation. Please feel free to talk through drafts of your presentation and even to rehearse a version before the class arrives.

The second assessment for the seminar involves weekly lab exercises, which involve applying causal inference ideas from class to real data using partially guided code scripts. Assignments involve real data and real R packages for causal inference; they will give some steps in full code, some steps in incomplete hints, and some steps will be left for you to complete. Finally, there will be questions about what we are trying to accomplish, what certain results mean, etc., that ask you to talk about the methods we're learning in your own words. Lab assignments for the entire quarter are currently available on the course github site (\url{https://github.com/jnseawright/PS406}).

The third and final assessment is a mock grant proposal that features a research design that shows mastery of at least one cutting-edge quantitative causal inference estimator from this class. The proposal will be evaluated based on the criteria listed for the Northwestern Graduate School's Graduate Research Grant (\url{https://www.tgs.northwestern.edu/funding/fellowships-and-grants/internal-fellowships-grants/graduate-research-grant.html}), and the format must meet the rules for the ``Description of the project'' section of a proposal for that grant --- five pages, double spaced, up to three pages of references/endnotes/figures --- with the exception that it does not need to already have IRB approval.

Class Materials (Required)

Imbens, Guido W. and Donald B. Rubin. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction 2015, 978-0521885881

Cunningham, Scott. 2021. Causal Inference: The Mixtape. Yale University Press. 1st Edition. ISBN: ‎ 978-0300251685

Huntington-Klein, Nick. 2022. The Effect: An Introduction to Research Design and Causality by available online in an extremely useful Markdown version at (\url{https://theeffectbook.net/}).

Enrollment Requirements

Enrollment Requirements: Reserved for Graduate Students.