Jennifer Hill

Causal inference is critical for education research because it informs decisions being made every day by students, teachers, administrators, and policy makers. Methodological advances for estimating causal effects have grown considerably in the past few decades, however, when several treatments, outcomes, or moderators are involved, each analysis is still generally considered in a standalone way. This piecemeal approach to causal inference generally leads to overall underestimation of our tr...

The purpose of this grant is to develop a highly scaffolded multi-purpose causal inference software package, thinkCausal, with the Bayesian Additive Regression Trees (BART) predictive algorithm as a foundation. This will allow education researchers from varied backgrounds to access and better understand these versatile estimation tools.

This training program prepared four researchers to (a) develop statistical methods required to meet future education research challenges and (b) teach other researchers how to use more advanced quantitative methods.