Causal Effect Estimation with Variational Inference and Latent Confounders

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This notebook demonstrates how to estimate the Average Treatment Effect (ATE) using variational
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). 
 The fundamental challenge in causal inference from…

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