Covariate balancing functional propensity score for functional treatments in cross-sectional observational studies

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. As the first attempt in the literature, the causal effect is studied for a functional variable as a treatment in cross-sectional observational studies. Despite the lack of a probability density function for the functional treatment, the propensity score is properly defined in terms of its top functional principal component scores which can represent the functional treatment approximately. 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.