TY - JOUR
T1 - Significance test for semiparametric conditional average treatment effects and other structural functions
AU - Zhou, Niwen
AU - Guo, Xu
AU - Zhu, Lixing
N1 - The authors gratefully acknowledge NSFC grants ( NSFC12131006 , 12322112 , 12071038 , 12301331 ) from the National Natural Science Foundation of China , a grant from the University Grants Council of Hong Kong ( HKBU12302720 ), and a grant from Natural Science Foundation of Guangdong Province ( 2023A1515010026 ).
Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2024/1
Y1 - 2024/1
N2 - The paper investigates a hypothesis testing problem concerning the potential additional contributions of other covariates to the structural function, given the known covariates. The structural function is the conditional expectation given covariates in which the response may depend on unknown nuisance functions. It includes classic regression functions and the conditional average treatment effects as illustrative instances. Based on Neyman's orthogonality condition, the proposed distance-based test exhibits the quasi-oracle property in the sense that the nuisance function asymptotically does not influence on the limiting distributions of the test statistic under both the null and alternatives. This novel test can effectively detect the local alternatives distinct from the null at the fastest possible rate in hypothesis testing. This is particularly noteworthy given the involvement of nonparametric estimation of the conditional expectation. Numerical studies are conducted to examine the performance of the test. In the real data analysis section, the proposed tests are applied to identify significantly explanatory covariates that are associated with AIDS treatment effects, yielding noteworthy insights.
AB - The paper investigates a hypothesis testing problem concerning the potential additional contributions of other covariates to the structural function, given the known covariates. The structural function is the conditional expectation given covariates in which the response may depend on unknown nuisance functions. It includes classic regression functions and the conditional average treatment effects as illustrative instances. Based on Neyman's orthogonality condition, the proposed distance-based test exhibits the quasi-oracle property in the sense that the nuisance function asymptotically does not influence on the limiting distributions of the test statistic under both the null and alternatives. This novel test can effectively detect the local alternatives distinct from the null at the fastest possible rate in hypothesis testing. This is particularly noteworthy given the involvement of nonparametric estimation of the conditional expectation. Numerical studies are conducted to examine the performance of the test. In the real data analysis section, the proposed tests are applied to identify significantly explanatory covariates that are associated with AIDS treatment effects, yielding noteworthy insights.
KW - Neyman orthogonality
KW - Precision medicine
KW - Treatment effect heterogeneity
UR - http://www.scopus.com/inward/record.url?scp=85171164679&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2023.107839
DO - 10.1016/j.csda.2023.107839
M3 - Journal article
AN - SCOPUS:85171164679
SN - 0167-9473
VL - 189
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
M1 - 107839
ER -