Counterfactual Generative Models for Time-Varying Treatments

arXiv:2305.15742v3 Announce Type: replace-cross
Abstract: Estimating the counterfactual outcome of treatment is essential for decision-making in public health and clinical science, among others. Often, treatments are administered in a sequential, time-varying manner, leading to an exponentially increased number of possible counterfactual outcomes. Furthermore, in modern applications, the outcomes are high-dimensional and conventional average treatment effect estimation fails to capture disparities in individuals. To tackle these challenges, we propose a novel conditional generative framework capable of producing counterfactual samples under time-varying …

abstract applications arxiv clinical counterfactual cs.lg decision generative generative models health making modern modern applications public public health science stat.me stat.ml treatment type