Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Structural Causal Interpretation Theorem   Human mental processes allow for qualitative reasoning about causality in
terms of mechanistic relations of the variables of interest, which we argue are
naturally described by structural causal model (SCM). Since interpretations are
being derived from mental models, the same applies for SCM. By defining a
metric space on SCM, we provide a theoretical perspective on the comparison of
mental models and thereby conclude that interpretations can be used for guiding
a learning system towards true causality. To this effect, we present a
theoretical analysis from first principles that results in a human-readable
interpretation scheme consistent with the provided causality that we name
structural causal interpretations (SCI). Going further, we prove that any
existing neural induction method (NIM) is in fact interpretable. Our first
experiment (E1) assesses the quality of such NIM-based SCI. In (E2) we observe
evidence for our conjecture on improved sample-efficiency for SCI-based
learning. After conducting a small user study, in (E3) we observe superiority
in human-based over NIM-based SCI in support of our initial hypothesis.

3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg