Causal Effect Estimation with Variational Inference and Latent Confounders

This notebook demonstrates how to estimate the Average Treatment Effect (ATE) using variational&#xA;inference in the presence of unobserved confounders. The approach is based on the tutorial by Robert Ness from his book: Causal AI book . We port his Pyro code to NumPyro (this was the objective for me to learn the details of the method). &#xA; The fundamental challenge in causal inference from…