Regression discontinuity designs for evaluating programs and policies
April 11 @ 12:00 pm - 4:00 pm$200
A 4-hour workshop taught by Brad Curs, Ph.D.
Causal inference workshop series discount!
Register for this along with our workshop on Introduction to matching and propensity score analysis for only $350 (would normally cost $450 for both). If you are interested in the workshop series, email us at email@example.com for discount codes.Register
This workshop provides an introduction to the practical application of regression discontinuity design in evaluating programs and policies. Regression discontinuity (RD) is an observational research design which can be used to make causal inference of program effects in non-experimental situations. Regression discontinuity is applied when program treatments are allocated based upon a pre-determined rule. For example, if a remediation intervention is provided to all students who scored below a certain threshold on an academic aptitude exam, or, a financial subsidy is provided to all applicants with a household income falling below a particular value. A primary advantage of regression discontinuity designs is that causal effects can be estimated when program benefits are distributed based upon the subject’s need for the intervention, rather than randomization in the case of an experiment.
By the end of the workshop, participants will understand the advantages of regression discontinuity design, how to estimate regression discontinuity designs across a number of statistical packages, and how to use data to check the validity of these regression discontinuity estimates to make causal inference. Participants will learn both visual and statistical techniques to estimate and evaluate regression discontinuity treatment effects. Participants will learn both sharp regression discontinuity techniques (used when subjects are compliant with treatment intent) and fuzzy regression discontinuity techniques (when subjects are not compliant with treatment intent). Most importantly, participants will be ready to identify opportunities to evaluate programs and policies using regression discontinuity designs and will be prepared to estimate program effects using any statistical software package.
Pricing and schedule
Time: Monday, April 11, 12PM to 4PM (EST)
Series discount – Register for this along with our workshop on Introduction to matching and propensity score analysis for only $350 (would normally cost $450 for both). If you are interested in the workshop series, email us at firstname.lastname@example.org for discount codes.
We offer $50 discounts for graduate students and $25 discounts for multiple workshop enrollments. Find out about our discounts here.
Although the main workshop material is scheduled for three hours, Dr. Curs will stay online for an additional hour or so, to ensure that he answers all questions. Time permitting, he will also answer questions about participants’ specific research projects. Participants can ask questions via chat, microphone, or telephone. In order to allow sufficient time for questions, the number of workshop participants is limited to 30.
Who should attend?
The target audience for this workshop is a range of researchers, including institutional researchers, market researchers, policy analysts, student affairs professionals, assessment professionals, graduate students, and faculty, who evaluate programs in their work. It is important that participants have a working knowledge of ordinary least squares.
If you need a refresher on ordinary least squares, consider enrolling in our refresher on multiple regression workshop. Software demonstrations will use Stata, but R code will also be included for participants who are using R for their research projects.
- What is regression discontinuity design, and when can it be applied?
- The advantages of regression discontinuity design over alternative research designs
- Estimating the sharp regression discontinuity design model (when subjects are compliant with treatment intent)
- Estimating the fuzzy regression discontinuity design model (when subjects are not compliant with treatment intent)
- Making functional form and bandwidth decisions
- Checking the assumptions of regression discontinuity designs
- Alternative (non-parametric) approaches to estimating regression discontinuity designs