Rebar: Reinforcing a Matching Estimator with Predictions from High-Dimensional Covariates

TL;DR

Rebar is a novel method that enhances causal matching estimates by leveraging high-dimensional covariate predictions to reduce bias without compromising the matching design.

Contribution

The paper introduces rebar, a new approach that uses machine learning predictions from unmatched control data to improve causal inference in matching designs.

Findings

Rebar reduces bias in causal estimates through high-dimensional covariate modeling.

Theoretical results support bias reduction properties of rebar.

Simulation studies demonstrate improved accuracy of causal estimates.

Abstract

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.