Kernel-based estimators for functional causal effects
This work extends causal inference to dynamic and non-linear domains, offering new tools for understanding complex treatment effects in functional data settings, though it appears incremental in its methodological advancements.
The authors tackled the problem of estimating causal effects in functional data settings, proposing kernel-based estimators that address high-dimensionality and model complexity, and demonstrated their utility in biomedical monitoring with complex temporal dynamics.
We propose causal effect estimators based on empirical Fréchet means and operator-valued kernels, tailored to functional data spaces. These methods address the challenges of high-dimensionality, sequential ordering, and model complexity while preserving robustness to treatment misspecification. Using structural assumptions, we obtain compact representations of potential outcomes, enabling scalable estimation of causal effects over time and across covariates. We provide both theoretical, regarding the consistency of functional causal effects, as well as empirical comparison of a range of proposed causal effect estimators. Applications to binary treatment settings with functional outcomes illustrate the framework's utility in biomedical monitoring, where outcomes exhibit complex temporal dynamics. Our estimators accommodate scenarios with registered covariates and outcomes, aligning them to the Fréchet means, as well as cases requiring higher-order representations to capture intricate covariate-outcome interactions. These advancements extend causal inference to dynamic and non-linear domains, offering new tools for understanding complex treatment effects in functional data settings.