A1361
Title: Doubly robust estimation of causal effects for random object outcomes with continuous treatments
Authors: Satarupa Bhattacharjee - University of Florida (United States)
Xiao Wu - Columbia University (United States)
Lingzhou Xue - Penn State University (United States)
Bing Li - The Pennsylvania State University (United States) [presenting]
Abstract: Causal inference is central to statistics and scientific discovery, enabling researchers to identify cause-and-effect relationships beyond associations. While traditionally studied within Euclidean spaces, contemporary applications increasingly involve complex, non-Euclidean data structures that reside in abstract metric spaces. The purpose is to introduce a novel framework for causal inference with continuous treatments applied to non-Euclidean data. To address the challenges posed by the lack of linear structures, Hilbert space embeddings of the metric spaces are leveraged to facilitate Frechet mean estimation and causal effect mapping. The framework can accommodate moderately high-dimensional vector-valued confounders and derive efficient influence functions for estimation to ensure both robustness and interpretability. Rigorous asymptotic properties of the cross-fitted estimators are established, and conformal inference techniques for counterfactual outcome prediction are employed.
