Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Tractability of Neural Causal Inference   Roth (1996) proved that any form of marginal inference with probabilistic
graphical models (e.g. Bayesian Networks) will at least be NP-hard. Introduced
and extensively investigated in the past decade, the neural probabilistic
circuits known as sum-product network (SPN) offers linear time complexity. On
another note, research around neural causal models (NCM) recently gained
traction, demanding a tighter integration of causality for machine learning. To
this end, we present a theoretical investigation of if, when, how and under
what cost tractability occurs for different NCM. We prove that SPN-based causal
inference is generally tractable, opposed to standard MLP-based NCM. We further
introduce a new tractable NCM-class that is efficient in inference and fully
expressive in terms of Pearl's Causal Hierarchy. Our comparative empirical
illustration on simulations and standard benchmarks validates our theoretical
proofs.

30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an