  In causal matching designs, some control subjects are often left unmatched,
and some covariates are often left unmodeled. This article introduces "rebar,"
a method using high-dimensional modeling to incorporate these commonly
discarded data without sacrificing the integrity of the matching design. After
constructing a match, a researcher uses the unmatched control subjects--the
remnant--to fit a machine learning model predicting control potential outcomes
as a function of the full covariate matrix. The resulting predictions in the
matched set are used to adjust the causal estimate to reduce confounding bias.
We present theoretical results to justify the method's bias-reducing properties
as well as a simulation study that demonstrates them. Additionally, we
illustrate the method in an evaluation of a school-level comprehensive
educational reform program in Arizona.

Methodology;Applications