| Title: | Statistical Theory for the RCT-YES Software: Design-Based Causal Inference for RCTs |
| Description: | This Second Edition report updates the First Edition published in June 2015 that presents the statistical theory underlying the RCT-YES software that estimates and reports impacts for RCTs for a wide range of designs used in social policy research. The preface to the new report summarizes the updates from the previous version. The report discusses a unified, non-parametric design-based approach for impact estimation using the building blocks of the Neyman-Rubin-Holland causal inference model that underlies experimental designs. This approach differs from the more model-based impact estimation methods that are typically used in education research. The report discusses impact and variance estimation, asymptotic distributions of the estimators, hypothesis testing, the inclusion of baseline covariates to improve precision, the use of weights, subgroup analyses, baseline equivalency analyses, and estimation of the complier average causal effect parameter. |
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| Cover Date: | June 2015 |
| Web Release: | June 2, 2015 |
| Print Release: | June 2, 2015 |
| Publication #: | NCEE 20154011 General Ordering Information |
| Center/Program: | NCEE |
| Authors: | Peter Z. Schochet: Mathematica Policy Research, Inc. |
| Type of Product: | Technical Methods Report |
| Keywords: | |
| Questions: |
For questions about the content of this Technical Methods Report, please contact: NCES WebMaster. |