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- W3213268526 abstract "Causal effect variational autoencoder (CEVAE) are trained to predict the outcome given observational treatment data, while uniform treatment variational autoencoders (UTVAE) are trained with uniform treatment distribution using importance sampling. In this paper, we show that using uniform treatment over observational treatment distribution leads to better causal inference by mitigating the distribution shift that occurs from training to test time. We also explore the combination of uniform and observational treatment distributions with inference and generative network training objectives to find a better training procedure for inferring treatment effect. Experimentally, we find that the proposed UTVAE yields better absolute average treatment effect error and precision in estimation of heterogeneous effect error than the CEVAE on synthetic and IHDP datasets." @default.
- W3213268526 created "2021-11-22" @default.
- W3213268526 creator A5047369861 @default.
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- W3213268526 date "2021-11-16" @default.
- W3213268526 modified "2023-09-25" @default.
- W3213268526 title "Causal Effect Variational Autoencoder with Uniform Treatment" @default.
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