Doubly Robust Estimation of Causal Effects for Random Object Outcomes with Continuous Treatment

Music City Center 

This project aims to extend Difference-in-Differences (DiD) methods in a potential outcome framework to study causal relationships for random object responses and continuous treatments in the presence of high-dimensional confounders, with a focus on large-scale observational studies. Motivated by assessing the causal link between air pollution and health outcomes going beyond traditional regression methods. We appropriately define the causal effects for varying levels of the continuous treatment by utilizing a Hilbert space embedding of the metric space valued outcomes, propose a doubly debiased estimator via data splitting, and analyze its asymptotic properties.

Non-Euclidean Data

Doubly robust estimation

Causal inference

Semiparametric efficiency

Embedding in Hilbert space 

Section on Nonparametric Statistics