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In this notebook, we demonstrate how to estimate Conditional Average Treatment Effects
(CATE) using a Causal Effect Variational Autoencoder (CEVAE) by implementing an example
from scratch in NumPyro . This approach is particularly useful when we suspect
the presence of unobserved confounders that affect both treatment assignment and outcomes. 
 Disclaimer : I am not an expert in…
In this notebook, we demonstrate how to estimate Conditional Average Treatment Effects
(CATE) using a Causal Effect Variational Autoencoder (CEVAE) by implementing an example
from scratch in NumPyro. This approach is particularly useful when we suspect
the presence of unobserved confounders that affect both treatment assignment and outcomes.
Disclaimer: I am not an expert in this specific approach, so please take all the results with a grain of salt and please do not hesitate to provide feedback.
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