Yuehao Bai, Liang Jiang, Joseph P. Romano, Azeem M. Shaikh, Yichong Zhang
arXiv 9 Feb 2023 · Econometrics · publishedJournal of Econometrics (2024) · 6 citations (OpenAlex)
arXiv:2302.04380 · PDF · DOI · OpenAlex · Extracted main text
This paper studies inference on the average treatment effect in experiments in which treatment status is determined according to "matched pairs" and it is additionally desired to adjust for observed, baseline covariates to gain further precision. By a "matched pairs" design, we mean that units are sampled i.i.d. from the population of interest, paired according to observed, baseline covariates and finally, within each pair, one unit is selected at random for treatment. Importantly, we presume that not all observed, baseline covariates are used in determining treatment assignment. We study a broad class of estimators based on a "doubly robust" moment condition that permits us to study estimators with both finite-dimensional and high-dimensional forms of covariate adjustment. We find that estimators with finite-dimensional, linear adjustments need not lead to improvements in precision relative to the unadjusted difference-in-means estimator. This phenomenon persists even if the adjustments are interacted with treatment; in fact, doing so leads to no changes in precision. However, gains in precision can be ensured by including fixed effects for each of the pairs. Indeed, we show that this adjustment is the "optimal" finite-dimensional, linear adjustment. We additionally study two estimators with high-dimensional forms of covariate adjustment based on the LASSO. For each such estimator, we show that it leads to improvements in precision relative to the unadjusted difference-in-means estimator and also provide conditions under which it leads to the "optimal" nonparametric, covariate adjustment. A simulation study confirms the practical relevance of our theoretical analysis, and the methods are employed to reanalyze data from an experiment using a "matched pairs" design to study the effect of macroinsurance on microenterprise.
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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 | Freedman, D. A (2008) On regression adjustments to experimental data | 0.928 | 4 | 4 | 100% |
| 2 | Bai, Y., Romano, J. P. and Shaikh, A. M (2022) Inference in Experiments With Matched Pairs self | 0.879 | 28 | 6 | 68% |
| 3 | Armstrong, T. B (2022) Asymptotic Efficiency Bounds for a Class of Experimental Designs | 0.843 | 3 | 3 | 100% |
| 4 | Cohen, P. L. and Fogarty, C. B (2023) No-harm calibration for generalized oaxaca-blinder estimators | 0.737 | 3 | 2 | 100% |
| 5 | Belloni, A., Chernozhukov, V., Fernández-Val, I. and Hansen, C (2017) Program evaluation and causal inference with high-dimensional data | 0.693 | 6 | 1 | 100% |
| 6 | Groh, M. and McKenzie, D (2016) Macroinsurance for microenterprises: A randomized experiment in post-revolution egypt | 0.693 | 5 | 1 | 100% |
| 7 | Cytrynbaum, M (2023) Covariate adjustment in stratified experiments | 0.585 | 3 | 1 | 100% |
| 8 | Chernozhukov, V., Chetverikov, D. and Kato, K (2017) Central limit theorems and bootstrap in high dimensions | 0.511 | 4 | 2 | 25% |
| 9 | Bickel, P. J., Ritov, Y. and Tsybakov, A. B (2009) Simultaneous analysis of Lasso and Dantzig selector | 0.511 | 2 | 2 | 50% |
| 10 | Jiang, L., Liu, X., Phillips, P. C. and Zhang, Y (2022) Bootstrap inference for quantile treatment effects in randomized experiments with matched pairs self | 0.511 | 2 | 1 | 100% |
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