Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High-dimensional learning of linear causal networks via inverse
  covariance estimation   We establish a new framework for statistical estimation of directed acyclic
graphs (DAGs) when data are generated from a linear, possibly non-Gaussian
structural equation model. Our framework consists of two parts: (1) inferring
the moralized graph from the support of the inverse covariance matrix; and (2)
selecting the best-scoring graph amongst DAGs that are consistent with the
moralized graph. We show that when the error variances are known or estimated
to close enough precision, the true DAG is the unique minimizer of the score
computed using the reweighted squared l_2-loss. Our population-level results
have implications for the identifiability of linear SEMs when the error
covariances are specified up to a constant multiple. On the statistical side,
we establish rigorous conditions for high-dimensional consistency of our
two-part algorithm, defined in terms of a "gap" between the true DAG and the
next best candidate. Finally, we demonstrate that dynamic programming may be
used to select the optimal DAG in linear time when the treewidth of the
moralized graph is bounded.

31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics