Daisuke Kurisu, Taisuke Otsu and Mengshan Xu

Published 25 October 2023

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Functional data and their analysis have become increasingly popular in various fields of data sciences. This paper considers estimation and inference of the average treatment effect under unconfoundedness when the covariates involve a functional variable, and propose the inverse probability weighting estimator, where the propensity score is estimated by utilizing a kernel estimator for functional variables. We establish the root-n consistency and asymptotic normality of the proposed estimator. Simulation studies illustrate usefulness of the proposed method.