Regression Discontinuity Designs
A 2-Day Seminar Taught by Rocío Titiunik, Ph.D.
To see a sample of the course materials, click here.
This two-day seminar focuses on methods for the analysis and interpretation of Regression Discontinuity (RD) designs. It will cover both introductory concepts and recent methodological developments.
The RD design is a non-experimental method that has high internal validity for estimating treatment effects. The design can be used when individuals are assigned to some treatment based entirely on a score—in Education, this score is usually referred to as a “pretest score”. This could be any quantitative measure, such as an exam grade, income, age, or cholesterol level. All individuals whose score exceeds a predetermined cutoff are offered the treatment, while all individuals below the cutoff are not offered the treatment. For example, if a scholarship is given only to students who score 90 or more points in an exam, the effect of the scholarship could be analyzed with a RD design.
After treatment, an outcome is measured for all individuals–the “posttest score” in Education–which could either be the same variable as the pretest score or a different measure. The analysis focuses on detecting possible discontinuities in the observed relationship between the pretest score and the outcome of interest at the cutoff, under appropriate continuity or local randomization assumptions.
RD designs appear naturally in cases where a policy is given to those who are most deserving or in greatest need. For example, a RD design might be implemented by ordering individuals from poorest to richest, and giving financial aid to the poorest individuals first until the budget has been exhausted. Although this would result in the treatment group being much poorer than the control group, the RD design relies on the assumption that near the cutoff, treated and control individuals would have had very similar outcomes in the absence of the treatment. Thus, one appealing feature of the RD design is that, if the required assumptions are met, it allows researchers to make internally valid causal inferences for those at the margin of switching from control to treatment assignment.
The course will discuss different assumptions under which the change in treatment status at the cutoff can be used to study causal treatment effects on outcomes of interest. The focus will be on methodology and empirical practice, not on theoretical results. The statistical and econometric theory underlying the results will be discussed at a conceptual, non-technical level.
The course will be based, in part, on the following two practical guides:
1. Cattaneo, Matias D., Nicolas Idrobo, and Rocío Titiunik. A Practical
Introduction to Regression Discontinuity Designs: Foundations. Forthcoming,
Cambridge University Press.
Click here for pre-publication draft.
2. Cattaneo, Matias D., Nicolas Idrobo, and Rocío Titiunik. A Practical
Introduction to Regression Discontinuity Designs: Extensions. In preparation,
Cambridge University Press.
Click here for preliminary draft.
Who should attend?
This seminar assumes that participants have elementary working knowledge of statistics, econometrics and policy evaluation. It will be useful, but not required, if participants are familiar with basic results from the literature on program evaluation and treatment effects at the level of Wooldridge (2010, Econometric Analysis of Cross Section and Panel Data, MIT Press). Nonetheless, the course is meant to be self-contained and most underlying statistics/econometrics concepts and results are introduced and explained in class.
There will be several empirical illustrations, using Stata for the analysis. In addition, all functions and packages are also available in R, a free and open-source statistical software environment. The following Stata/R modules/commands will be used:
- rdrobust: RD analysis employing local polynomial and partitioning methods.
- rddensity: Manipulation testing for RD designs.
- rdlocrand: RD analysis employing randomization inference methods.
- rdmulti: Estimation and inference for RD designs with multiple cutoffs and multiple scores.
- rdpower: Power and sample size calculations using robust bias-corrected local polynomial inference methods.
All packages and associated references and help files can be found here.
All replication files for the empirical examples are available here.
Location, Format and materials
The class will meet from 9 am to 5 pm each day with a 1-hour lunch break at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103.
Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.
Registration and Lodging
The fee of $995.00 includes all seminar materials. The early registration fee of $895.00 is available until November 6.
If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).
Lodging Reservation Instructions
A block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $138 per night. This location is about a 5-minute walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code SH1205 or click here. For guaranteed rate and availability, you must reserve your room no later than Tuesday, November 5, 2019.
If you need to make reservations after the cut-off date, you may call Club Quarters directly and ask for the “Statistical Horizons” rate (do not use the code or mention a room block) and they will try to accommodate your request.
1. Introduction to causal inference and program evaluation.
2. Introduction to RD designs; graphical illustration via RD plots.
3. Standard local polynomial methods and bandwidth selection for RD
4. Robust local polynomial methods for RD analysis; fuzzy RD designs.
5. Local randomization methods for RD analysis.
6. Falsification of RD designs.
7. Advanced RD topics: RD designs with discrete running variables and RD
analysis with additional covariates. If time permits: RD designs with multiple
cutoffs and geographic RD designs; RD extrapolation; power calculations
for RD analysis.