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Functional Data Analysis with Causation in Observational Studies: Covariate Balancing Functional Propensity Score for Functional Treatments
Functional data analysis, which handles data arising from curves, surfaces,
volumes, manifolds and beyond in a variety of scientific fields, is a rapidly
developing area in modern statistics and data science in the recent decades.
The effect of a functional variable on an outcome is an essential theme in
functional data analysis, but a majority of related studies are restricted to
correlational effects rather than causal effects. This paper makes the first
attempt to study the causal effect of a functional variable as a treatment in
observational studies. Despite the lack of a probability density function for
the functional treatment, the propensity score is properly defined in terms of
a multivariate substitute. Two covariate balancing methods are proposed to
estimate the propensity score, which minimize the correlation between the
treatment and covariates. The appealing performance of the proposed method in
both covariate balance and causal effect estimation is demonstrated by a
simulation study. The proposed method is applied to study the causal effect of
body shape on human visceral adipose tissue