Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Causal Effect Variational Autoencoder with Uniform Treatment   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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The categories of this paper are: nlin
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
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The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys
18
The categories of this paper are: econ
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
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The categories of this paper are: q-bio
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The categories of this paper are: atom-ph
16
The categories of this paper are: stat