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ERIC Number: EJ1166278
Record Type: Journal
Publication Date: 2018-Feb
Pages: 29
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1076-9986
EISSN: N/A
Rebar: Reinforcing a Matching Estimator with Predictions from High-Dimensional Covariates
Sales, Adam C.; Hansen, Ben B.; Rowan, Brian
Journal of Educational and Behavioral Statistics, v43 n1 p3-31 Feb 2018
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.
SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Publication Type: Journal Articles; Reports - Research
Education Level: High Schools; Secondary Education
Audience: N/A
Language: English
Sponsor: Institute of Education Sciences (ED)
Authoring Institution: N/A
Identifiers - Location: Arizona
IES Funded: Yes
Grant or Contract Numbers: R305B100012