Regression Discontinuity Design

Prof. Rocío Titiunik

Starting October 3rd, 2023

This course covers methods for the analysis and interpretation of the Regression Discontinuity (RD) design, a non-experimental strategy to study treatment effects that can be used when units receive a treatment based on a score and a cutoff. The course covers methods for estimation, inference, and falsification of RD treatment effects using two different approaches: the continuity-based framework, implemented with local polynomials, and the local randomization framework, implemented with standard tools from the analysis of experiments. The focus is on conceptual understanding of the underlying methodological issues and effective empirical implementation. Every topic is illustrated with the analysis of RD examples using real-world data, walking through R and Stata codes that fully implement all the methods discussed. At the end of the course, participants will have acquired the necessary skills to rigorously interpret, visualize, validate, estimate, and characterize the uncertainty of RD treatment effects.

All course material is available free and open source via our Github Repository .

Register Today

Sign-up today to ensure access to this workshop.

WHAT'S INCLUDED

  • Attendance to the workshop with Prof. Rocío Titiunik.
  • Complete set of example code to implement the methods discussed.
  • PDF lecture slides and video recordings for later reference.
$595 USD + Fees
Buy Your Ticket
$50 for Students • $95 for Non Tenure-track Faculty • $1 for Low-income Countries

Our workshop will bring you to the cutting edge

October 3rd

Day 1 • 6pm-9pm EST

  1. Sharp RD design: introduction and graphical illustration with RD plots
  2. Continuity based RD analysis
  3. Estimation of RD effects with local polynomials
  4. Optimal bandwidth selection

October 4th

Day 2 • 6pm-9pm EST

  1. Continuity based RD analysis, continued
  2. Robust confidence intervals based on local polynomials
  3. Local Randomization RD analysis
  4. Inferences based on Fisherian methods
  5. Window selection based on covariates
  6. Inferences based on large-sample methods

October 5th

Day 3 • 6pm-9pm EST

  1. Falsification of RD assumptions: density and covariate balance tests
  2. Imperfect compliance: The Fuzzy RD design

Who will be hosting this session?

Prof. Rocío Titiunik
Rocío Titiunik is Professor of Politics at Princeton University, where she is also an associated faculty with the Department of Operations Research and Financial Engineering, the Center for Statistics and Machine Learning, the Program in Latin American Studies, the Center for the Study of Democratic Politics, and the Research Program in Political Economy. She specializes in quantitative methodology for the social and behavioral sciences, with emphasis on quasi-experimental methods for causal inference and program evaluation. Her research interests lie at the intersection of political economy, political science, statistics, and data science, particularly on the development and application of quantitative methods to the study of political institutions. Her recent methodological research includes the development of statistical methods for regression discontinuity (RD) designs. Her recent substantive research centers on democratic accountability and the role of party systems in developing democracies.

Frequently Asked Questions

Are discounts available?

Yes! Students, postdocs, predocs and residents of middle-income countries can attend for $50 plus a few dollars in fees. Non-tenure track faculty can attend for $95. To receive your promo code, please include a photo of your student ID. International folks from low-income countries can attend for $1. To receive promo codes, email us at causalinf@mixtape.consulting.

How do I access the material I need for the course?

The course material will be availabe forever on Github. We will also send you links to the video recordings on Vimeo after the workshop is completed.

How long will it take me to master this?

That's a great question. Causal inference, and econometrics more generally, is largely a “returns to experience” type of skill as much as it is a returns to education. The best way for you to learn anything in these classes is to work on projects that require it. Our class is designed as a fast track to both.

Will we practice programming?

Yes, I will distribute assignments with readings with directions the night before. We will help each other in Discord, asking questions, pointing out mistakes I'm making, and helping one another problem solve. I will usually assign more than we can do that faster workers always have something to work on. And in the end, I will distribute the solutions. It'll be fun I promise!

Will there be recordings?

We will upload recordings to Vimeo and they will be password protected, so that only attendees can watch the videos.

I'm nervous that I can't handle the difficulty of the class.

Don't be. I'm a good teacher. If I can learn this, so can you.