Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Moment-Matching Graph-Networks for Causal Inference   In this note we explore a fully unsupervised deep-learning framework for
simulating non-linear structural equation models from observational training
data. The main contribution of this note is an architecture for applying
moment-matching loss functions to the edges of a causal Bayesian graph,
resulting in a generative conditional-moment-matching graph-neural-network.
This framework thus enables automated sampling of latent space conditional
probability distributions for various graphical interventions, and is capable
of generating out-of-sample interventional probabilities that are often
faithful to the ground truth distributions well beyond the range contained in
the training set. These methods could in principle be used in conjunction with
any existing autoencoder that produces a latent space representation containing
causal graph structures.

6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con