John Engberg

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.

The project developed methods for estimation and inference in education research when actions by participants impact validity and availability of data. Specifically, the researchers addressed the impacts of participant actions in two research designs: differential attrition in lottery designs and manipulation of the assignment variable in regression discontinuity designs. The researchers used data from the Pittsburgh Public School’s magnet program to address attrition in lottery design...