Regression-Adjusted Estimation of Quantile Treatment Effects under Covariate-Adaptive Randomizations

Liang Jiang, Xiaobin Liu, Peter Phillips and Yichong Zhang
Additional contact information
Liang Jiang: Singapore Management University
Xiaobin Liu: School of Economics, Academy of Financial Research, and Institute for Fiscal Big-Data & Policy of Zhejiang University
Yichong Zhang: Singapore Management University

No 2288, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: This paper examines regression-adjusted estimation and inference of unconditional quantile treatment effects (QTEs) under covariate-adaptive randomizations (CARs). Datasets from field experiments usually contain extra baseline covariates in addition to the strata indicators. We propose to incorporate these extra covariates via auxiliary regressions in the estimation and inference of unconditional QTEs. We establish the consistency, limit distribution, and validity of the multiplier bootstrap of the QTE estimator under CARs. The auxiliary regression may be estimated parametrically, nonparametrically, or via regularization when the data are high-dimensional. Even when the auxiliary regression is misspecified, the proposed bootstrap inferential procedure still achieves the nominal rejection probability in the limit under the null. When the auxiliary regression is correctly specified, the regression-adjusted estimator achieves the minimum asymptotic variance. We also derive the optimal pseudo true values for the potentially misspecified parametric model that minimize the asymptotic variance of the corresponding QTE estimator. Our estimation and inferential methods can be implemented without tuning parameters and they allow for common choices of auxiliary regressions such as linear, probit and logit regressions despite the fact that these regressions may be misspecified. Finite-sample performance of the new estimation and inferential methods is assessed in simulations and an empirical application studying the impact of child health and nutrition on educational outcomes is included.

Keywords: Covariate-adaptive randomization; High-dimensional data; Regression adjustment; Quantile treatment effects (search for similar items in EconPapers)
JEL-codes: C14 C21 I21 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2021-05
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-sea
Note: Includes supplemental material
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://cowles.yale.edu/sites/default/files/files/pub/d22/d2288-a.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found

Related works:
Journal Article: Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations (2023)
Working Paper: Regression-Adjusted Estimation of Quantile Treatment Effects under Covariate-Adaptive Randomizations (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cwl:cwldpp:2288

Ordering information: This working paper can be ordered from
Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA
The price is None.

Access Statistics for this paper

More papers in Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University Yale University, Box 208281, New Haven, CT 06520-8281 USA. Contact information at EDIRC.
Bibliographic data for series maintained by Brittany Ladd ().