Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Interventional Sum-Product Networks: Causal Inference with Tractable
  Probabilistic Models   While probabilistic models are an important tool for studying causality,
doing so suffers from the intractability of inference. As a step towards
tractable causal models, we consider the problem of learning interventional
distributions using sum-product networks (SPNs) that are over-parameterized by
gate functions, e.g., neural networks. Providing an arbitrarily intervened
causal graph as input, effectively subsuming Pearl's do-operator, the gate
function predicts the parameters of the SPN. The resulting interventional SPNs
are motivated and illustrated by a structural causal model themed around
personal health. Our empirical evaluation on three benchmark data sets as well
as a synthetic health data set clearly demonstrates that interventional SPNs
indeed are both expressive in modelling and flexible in adapting to the
interventions.

9
The categories of this paper are: nlin
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc