Causal Effect Variational Autoencoder with Uniform Treatment

• URL: http://arxiv.org/abs/2111.08656v1
• Date: Tue, 16 Nov 2021 17:40:57 GMT
• Title: Causal Effect Variational Autoencoder with Uniform Treatment
• Authors: Daniel Jiwoong Im, Kyunghyun Cho, Narges Razavian
• Abstract summary: Causal effect variational autoencoder (CEVAE) are trained to predict the outcome given observational treatment data. Uniform treatment variational autoencoders (UTVAE) are trained with uniform treatment distribution using importance sampling.
• Score: 50.895390968371665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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