arXiv 7 Feb 2023 · Econometrics · publishedQuantitative Economics (2024) · 6 citations (OpenAlex)
arXiv:2302.03687 · PDF · DOI · OpenAlex · Extracted main text
This paper studies covariate adjusted estimation of the average treatment effect in stratified experiments. We work in a general framework that includes matched tuples designs, coarse stratification, and complete randomization as special cases. Regression adjustment with treatment-covariate interactions is known to weakly improve efficiency for completely randomized designs. By contrast, we show that for stratified designs such regression estimators are generically inefficient, potentially even increasing estimator variance relative to the unadjusted benchmark. Motivated by this result, we derive the asymptotically optimal linear covariate adjustment for a given stratification. We construct several feasible estimators that implement this efficient adjustment in large samples. In the special case of matched pairs, for example, the regression including treatment, covariates, and pair fixed effects is asymptotically optimal. We also provide novel asymptotically exact inference methods that allow researchers to report smaller confidence intervals, fully reflecting the efficiency gains from both stratification and adjustment. Simulations and an empirical application demonstrate the value of our proposed methods.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Winston Lin (2013) Agnostic notes on regression adjustments to experimental data: Reexamining freedman's critique | 1.000 | 8 | 3 | 100% |
| 2 | Ceren Baysan (2022) Persistent polarizing effects of persuasion: Experimental evidence from turkey | 0.874 | 10 | 2 | 100% |
| 3 | Guido W. Imbens and Donald B. Rubin (2015) Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction | 0.874 | 5 | 2 | 100% |
| 4 | Guido Imbens and Joshua D. Angrist (1994) Identification and estimation of local average treatment effects | 0.737 | 3 | 3 | 67% |
| 5 | Yuehao Bai, Joseph P. Romano, and Azeem M. Shaikh (2021) Inference in experiments with matched pairs | 0.737 | 3 | 2 | 100% |
| 6 | Xin Lu and Hanzhong Liu (2024) Tyranny-of-the-minority regression adjustment in randomized experiments | 0.737 | 3 | 2 | 100% |
| 7 | Ke Zhu, Hanzhong Liu, and Yuehan Yang (2024) Design-based theory for lasso adjustment in randomized block experiments with a general blocking scheme | 0.737 | 3 | 2 | 100% |
| 8 | Max Cytrynbaum (2023) Optimal stratification of survey experiments self | 0.705 | 20 | 6 | 35% |
| 9 | Tim Armstrong (2022) Asymptotic efficiency bounds for a class of experimental designs | 0.644 | 2 | 2 | 100% |
| 10 | Colin B. Fogarty (2018) Regression-assisted inference for the average treatment effect in paired experiments | 0.644 | 2 | 2 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.