TY - JOUR

T1 - Outcome regression-based estimation of conditional average treatment effect

AU - Li, Lu

AU - Zhou, Niwen

AU - Zhu, Lixing

N1 - The first two authors are co-first authors. The research was supported by a grant from the University Grants Council of Hong Kong (HKBU12302720) and a grant from the National Natural Science Foundation of China (NSFC12131006).
Publisher Copyright:
© 2022, The Institute of Statistical Mathematics, Tokyo.

PY - 2022/10

Y1 - 2022/10

N2 - The research is about a systematic investigation on the following issues. First, we construct different outcome regression-based estimators for conditional average treatment effect under, respectively, true, parametric, nonparametric and semiparametric dimension reduction structure. Second, according to the corresponding asymptotic variance functions when supposing the models are correctly specified, we answer the following questions: what is the asymptotic efficiency ranking about the four estimators in general? how is the efficiency related to the affiliation of the given covariates in the set of arguments of the regression functions? what do the roles of bandwidth and kernel function selections play for the estimation efficiency; and in which scenarios should the estimator under semiparametric dimension reduction regression structure be used in practice? Meanwhile, the results show that any outcome regression-based estimation should be asymptotically more efficient than any inverse probability weighting-based estimation. Several simulation studies are conducted to examine the finite sample performances of these estimators, and a real dataset is analyzed for illustration.

AB - The research is about a systematic investigation on the following issues. First, we construct different outcome regression-based estimators for conditional average treatment effect under, respectively, true, parametric, nonparametric and semiparametric dimension reduction structure. Second, according to the corresponding asymptotic variance functions when supposing the models are correctly specified, we answer the following questions: what is the asymptotic efficiency ranking about the four estimators in general? how is the efficiency related to the affiliation of the given covariates in the set of arguments of the regression functions? what do the roles of bandwidth and kernel function selections play for the estimation efficiency; and in which scenarios should the estimator under semiparametric dimension reduction regression structure be used in practice? Meanwhile, the results show that any outcome regression-based estimation should be asymptotically more efficient than any inverse probability weighting-based estimation. Several simulation studies are conducted to examine the finite sample performances of these estimators, and a real dataset is analyzed for illustration.

KW - Asymptotic variance

KW - Conditional average treatment effect

KW - Regression causal effect

KW - Sufficient dimension reduction

UR - http://www.scopus.com/inward/record.url?scp=85129186945&partnerID=8YFLogxK

U2 - 10.1007/s10463-022-00821-x

DO - 10.1007/s10463-022-00821-x

M3 - Journal article

AN - SCOPUS:85129186945

SN - 0020-3157

VL - 74

SP - 987

EP - 1041

JO - Annals of the Institute of Statistical Mathematics

JF - Annals of the Institute of Statistical Mathematics

IS - 5

ER -