Unbiased Regression-Adjusted Estimation of Average Treatment Effects in Randomized Controlled Trials
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915985170104320 |
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| author | Abadie, Alberto Ghadiri, Mehrdad Jadbabaie, Ali JafariNodeh, Mahyar |
| author_facet | Abadie, Alberto Ghadiri, Mehrdad Jadbabaie, Ali JafariNodeh, Mahyar |
| contents | This article introduces a leave-one-out regression adjustment (LOORA) for estimating average treatment effects in randomized controlled trials. In finite samples, LOORA removes the bias of conventional regression adjustment and yields exact variance formulas for regression-adjusted Horvitz-Thompson and difference-in-means estimators. Ridge regularization curbs the influence of high-leverage observations, improving stability and precision in small samples. In large samples, LOORA matches the variance of the regression-adjusted estimator in Lin (2013) while remaining exactly unbiased. Two within-subject experimental applications, each providing a realistic joint distribution of potential outcomes as ground truth, show that LOORA removes substantial bias and achieves confidence interval coverage close to the nominal level. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_03236 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unbiased Regression-Adjusted Estimation of Average Treatment Effects in Randomized Controlled Trials Abadie, Alberto Ghadiri, Mehrdad Jadbabaie, Ali JafariNodeh, Mahyar Econometrics Methodology 62J07, 62P20 G.3 This article introduces a leave-one-out regression adjustment (LOORA) for estimating average treatment effects in randomized controlled trials. In finite samples, LOORA removes the bias of conventional regression adjustment and yields exact variance formulas for regression-adjusted Horvitz-Thompson and difference-in-means estimators. Ridge regularization curbs the influence of high-leverage observations, improving stability and precision in small samples. In large samples, LOORA matches the variance of the regression-adjusted estimator in Lin (2013) while remaining exactly unbiased. Two within-subject experimental applications, each providing a realistic joint distribution of potential outcomes as ground truth, show that LOORA removes substantial bias and achieves confidence interval coverage close to the nominal level. |
| title | Unbiased Regression-Adjusted Estimation of Average Treatment Effects in Randomized Controlled Trials |
| topic | Econometrics Methodology 62J07, 62P20 G.3 |
| url | https://arxiv.org/abs/2511.03236 |