A2049
Title: Robust causal effect estimation in high dimensional survival analysis via nonparametric learning
Authors: Shanshan Ding - University of Delaware (United States) [presenting]
Zhezhen Jin - Columbia University (United States)
Abstract: Causal inference is an important tool for making inferences about causal relationships between variables. A robust nonparametric framework for estimating causal treatment effects in high-dimensional survival analysis is developed. The proposed framework is flexible and alleviates assumptions in existing causal survival analysis. It allows the covariate dimension to grow exponentially fast with the sample size without imposing stringent model or distributional assumptions for estimating the causal effect. The effectiveness of the methods is demonstrated through both theoretical and numerical studies.
