Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Conditions and Assumptions for Constraint-based Causal Structure
  Learning   This paper formalizes constraint-based structure learning of the "true"
causal graph from observed data when unobserved variables are also existent. We
provide conditions for a "natural" family of constraint-based
structure-learning algorithms that output graphs that are Markov equivalent to
the causal graph. Under the faithfulness assumption, this natural family
contains all exact structure-learning algorithms. More importantly, we provide
clear and testable assumptions, as an alternative to faithfulness, under which
any natural structure-learning algorithm outputs Markov equivalent graphs to
the causal graph. We provide these definitions and results for the general
class of models under the assumption that the distribution is Markovian to the
true causal graph, and we specialize the definitions and results for structural
causal models.

22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics