Statistical inference of heterogeneous treatment effect based on single-index model

Sanying Feng, Kaidi Kong, Yinfei Kong, Gaorong Li and Zhaoliang Wang

Computational Statistics & Data Analysis, 2022, vol. 175, issue C

Abstract: The heterogeneous treatment effect (HTE) is estimated by using the semiparametric regression method. Firstly, a flexible semiparametric single-index model is considered by assuming the nonparametric link function and the interaction between treatment and covariates, and the index parameter vector and the unknown link function are estimated by using the rMAVE method. Then a HTE estimator can be obtained based on the estimators of index parameter vector and the link function. The consistency and asymptotic normality of the HTE estimator are established under some regularity conditions. Secondly, a hypothesis test is developed for the existence of HTE, and the bootstrap procedure is utilized to evaluate the null distribution of test statistic. Finally, simulation studies and a real data analysis are conducted to assess the performance of our proposed method.

Keywords: Causal inference; Heterogeneous treatment effect; Propensity score; rMAVE; Single-index model (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947322001347
Full text for ScienceDirect subscribers only.

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:eee:csdana:v:175:y:2022:i:c:s0167947322001347

DOI: 10.1016/j.csda.2022.107554

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().