Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Critical Look at the Consistency of Causal Estimation With Deep Latent
  Variable Models   Using deep latent variable models in causal inference has attracted
considerable interest recently, but an essential open question is their ability
to yield consistent causal estimates. While they have demonstrated promising
results and theory exists on some simple model formulations, we also know that
causal effects are not even identifiable in general with latent variables. We
investigate this gap between theory and empirical results with analytical
considerations and extensive experiments under multiple synthetic and
real-world data sets, using the causal effect variational autoencoder (CEVAE)
as a case study. While CEVAE seems to work reliably under some simple
scenarios, it does not estimate the causal effect correctly with a misspecified
latent variable or a complex data distribution, as opposed to its original
motivation. Hence, our results show that more attention should be paid to
ensuring the correctness of causal estimates with deep latent variable models.

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The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
3
The categories of this paper are: physics
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
2
The categories of this paper are: math
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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an