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 |
| 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. |
|---|---|
| ISSN: | 1076-9986 |
| DOI: | 10.3102/1076998617731518 |
