5 papers
A Test for Treatment Heterogeneity under a Distributional Difference-in-Difference Framework
Satarupa Bhattacharjee, Bing Li, Lingzhou Xue
We develop a novel distributional Difference-in-Differences (DiD) framework to capture treatment heterogeneity across outcome distributions. By leveraging optimal transport, we use…
Kernel Single-Index Bandits: Estimation, Inference, and Learning
Sakshi Arya, Satarupa Bhattacharjee, Bharath K. Sriperumbudur
We study contextual bandits with finitely many actions in which the reward of each arm follows a single-index model with an arm-specific index parameter and an unknown nonparametri…
Variable Selection for Additive Global Fréchet Regression
Haoyi Yang, Satarupa Bhattacharjee, Lingzhou Xue +1
We present a novel framework for variable selection in Fréchet regression with responses in general metric spaces, a setting increasingly relevant for analyzing non-Euclidean data…
Doubly robust estimation of causal effects for random object outcomes with continuous treatments
Satarupa Bhattacharjee, Bing Li, Xiao Wu +1
Causal inference is central to statistics and scientific discovery, enabling researchers to identify cause-and-effect relationships beyond associations. While traditionally studied…
Nonlinear global Fréchet regression for random objects via weak conditional expectation
Satarupa Bhattacharjee, Bing Li, Lingzhou Xue
Random objects are complex non-Euclidean data taking value in general metric space, possibly devoid of any underlying vector space structure. Such data are getting increasingly abu…