2021
Causal Modeling with Stochastic Confounders
AISTATS 2021poster
This work extends causal inference in temporal models with stochastic confounders. We propose a new approach to variational estimation of causal inference based on a representer theorem with a random input space. We estimate causal effects involving latent confounders that may be interdependent and…