Abstract
To estimate the true treatment effect on a censored outcome in observational studies, potential confounding effect and complex heterogeneity in the treatment assignment have to be properly adjusted. In this article, we demonstrate that the partial least squares method could be a valuable tool in this regard. It is showed that the partial least squares method not only can adequately account for the heterogeneity in treatment assignment, but also be robust to treatment assignment model misspecifications. Numerical results show that the partial least squares estimator is more efficient and robust. A real data set is analyzed to illustrate the proposed method.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
