Intact-VAE: Estimating Treatment Effects under Unobserved Confounding

• URL: http://arxiv.org/abs/2101.06662v2
• Date: Wed, 17 Feb 2021 01:12:31 GMT
• Title: Intact-VAE: Estimating Treatment Effects under Unobserved Confounding
• Authors: Pengzhou Wu and Kenji Fukumizu
• Abstract summary: We propose Intact-VAE, a new variant of variational autoencoder (VAE) motivated by the prognostic score that is sufficient for identifying treatment effects. We theoretically show that, under certain settings, treatment effects are identified by our model, and further, based on the identifiability of our model, our VAE is a consistent estimator with representation balanced for treatment groups.
• Score: 21.33872753593482
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As an important problem of causal inference, we discuss the identification and estimation of treatment effects under unobserved confounding. Representing the confounder as a latent variable, we propose Intact-VAE, a new variant of variational autoencoder (VAE), motivated by the prognostic score that is sufficient for identifying treatment effects. We theoretically show that, under certain settings, treatment effects are identified by our model, and further, based on the identifiability of our model (i.e., determinacy of representation), our VAE is a consistent estimator with representation balanced for treatment groups. Experiments on (semi-)synthetic datasets show state-of-the-art performance under diverse settings.

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