High-Dimensional Regression Adjustment Estimation for Average Treatment Effect with Highly Correlated Covariates

Zeyu Diao, Lili Yue (), Fanrong Zhao and Gaorong Li
Additional contact information
Zeyu Diao: School of Statistics, Beijing Normal University, Beijing 100875, China
Lili Yue: School of Statistics and Data Science, Nanjing Audit University, Nanjing 211815, China
Fanrong Zhao: School of Mathematical Science, Shanxi University, Taiyuan 030006, China
Gaorong Li: School of Statistics, Beijing Normal University, Beijing 100875, China

Mathematics, 2022, vol. 10, issue 24, 1-18

Abstract: Regression adjustment is often used to estimate average treatment effect (ATE) in randomized experiments. Recently, some penalty-based regression adjustment methods have been proposed to handle the high-dimensional problem. However, these existing high-dimensional regression adjustment methods may fail to achieve satisfactory performance when the covariates are highly correlated. In this paper, we propose a novel adjustment estimation method for ATE by combining the semi-standard partial covariance (SPAC) and regression adjustment methods. Under some regularity conditions, the asymptotic normality of our proposed SPAC adjustment ATE estimator is shown. Some simulation studies and an analysis of HER2 breast cancer data are carried out to illustrate the advantage of our proposed SPAC adjustment method in addressing the highly correlated problem of the Rubin causal model.

Keywords: average treatment effect; highly correlated covariates; regression adjustment; rubin causal model; semi-standard partial covariance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/24/4715/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/24/4715/ (text/html)

Related works:
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:gam:jmathe:v:10:y:2022:i:24:p:4715-:d:1000755

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().