Grant Closed

Using Machine Learning Methods to Improve Regression Discontinuity Designs

NCER

Program: Statistical and Research Methodology in Education

Program topic(s): Early Career

Award amount: $224,999

Principal investigator: Isaac Opper

Awardee:

RAND Corporation

Year: 2020

Project type:

Methodological Innovation

Award number: R305D200008

Purpose

The purpose of this project is to develop an approach that incorporates machine learning methods into a regression discontinuity design to improve precision and reduce bias in the treatment effect estimates.

Project Activities

The new approach will use ridge regression to obtain residuals of the outcome, with the residuals then used in a typical regression discontinuity analysis. After initial development, the researchers will extend the software to calculate standard errors in a less computationally intensive manner, to estimate treatment effect heterogeneity, and to allow for random effects at the classroom level and/or the school level.

People and institutions involved

IES program contact(s)

Allen Ruby

Associate Commissioner for Policy and Systems

NCER

Products and publications

Opper, Isaac M., & Özek, U.. (2023). A Global Regression Discontinuity Design: Theory and Application to Grade Retention Policies. (EdWorkingPaper: 23-798). Retrieved from Annenberg Institute at Brown University: https://doi.org/10.26300/hq2t-7x64

Supplemental information

Co-Principal Investigators: Engberg, John; Johnston, William

The design of the study features simulations to compare the new technique to existing methods and an analysis of the New York City administrative data in order to provide an applied demonstration. This project will result in journal publications of the theoretical results, conference presentations, a how-to guide for the new method, and software for conducting the new method in R, Stata, and potentially Python. The research team will test the user-friendliness of the software packages on graduate students.

Questions about this project?

To answer additional questions about this project or provide feedback, please contact the program officer.

Using Machine Learning Methods to Improve Regression Discontinuity Designs

Purpose

Project Activities

People and institutions involved

IES program contact(s)

Allen Ruby

Products and publications

Supplemental information

Questions about this project?

Questions about this project?

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