  Domain adaptation and covariate shift are big issues in deep learning and
they ultimately affect any causal inference algorithms that rely on deep neural
networks. Causal effect variational autoencoder (CEVAE) is trained to predict
the outcome given observational treatment data and it suffers from the
distribution shift at test time. In this paper, we introduce uniform treatment
variational autoencoders (UTVAE) that are trained with uniform treatment
distribution using importance sampling and 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 effects. Experimentally, we
find that the proposed UTVAE yields better absolute average treatment effect
error and precision in the estimation of heterogeneous effect error than the
CEVAE on synthetic and IHDP datasets.
