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

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Title: Rebar: Reinforcing a Matching Estimator with Predictions from High-Dimensional Covariates
Language: English
Authors: Sales, Adam C., Hansen, Ben B., Rowan, Brian
Source: Journal of Educational and Behavioral Statistics. Feb 2018 43(1):3-31.
Availability: 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
Peer Reviewed: Y
Page Count: 29
Publication Date: 2018
Sponsoring Agency: Institute of Education Sciences (ED)
Contract Number: R305B100012
Document Type: Journal Articles
Reports - Research
Education Level: High Schools
Secondary Education
Descriptors: Computation, Prediction, Models, Data, Statistical Bias, Statistical Analysis, Educational Change, Validity, High Schools, Regression (Statistics)
Geographic Terms: Arizona
DOI: 10.3102/1076998617731518
ISSN: 1076-9986
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.
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2018
Accession Number: EJ1166278
Database: ERIC
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  Data: 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.
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      – SubjectFull: Prediction
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      – SubjectFull: Models
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      – SubjectFull: Arizona
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      – TitleFull: Rebar: Reinforcing a Matching Estimator with Predictions from High-Dimensional Covariates
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