Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CausalVAE: Disentangled Representation Learning via Neural Structural
  Causal Models   Learning disentanglement aims at finding a low dimensional representation
which consists of multiple explanatory and generative factors of the
observational data. The framework of variational autoencoder (VAE) is commonly
used to disentangle independent factors from observations. However, in real
scenarios, factors with semantics are not necessarily independent. Instead,
there might be an underlying causal structure which renders these factors
dependent. We thus propose a new VAE based framework named CausalVAE, which
includes a Causal Layer to transform independent exogenous factors into causal
endogenous ones that correspond to causally related concepts in data. We
further analyze the model identifiabitily, showing that the proposed model
learned from observations recovers the true one up to a certain degree.
Experiments are conducted on various datasets, including synthetic and real
word benchmark CelebA. Results show that the causal representations learned by
CausalVAE are semantically interpretable, and their causal relationship as a
Directed Acyclic Graph (DAG) is identified with good accuracy. Furthermore, we
demonstrate that the proposed CausalVAE model is able to generate
counterfactual data through "do-operation" to the causal factors.

12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org