A Critical Look At The Identifiability of Causal Effects with Deep Latent Variable Models

12 Feb 2021 Severi Rissanen Pekka Marttinen

Using deep latent variable models in causal inference has attracted considerable interest recently, but an essential open question is their identifiability. While they have yielded promising results and theory exists on the identifiability of some simple model formulations, we also know that causal effects cannot be identified in general with latent variables... (read more)

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METHOD TYPE
Causal Inference
AutoEncoder
Generative Models