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Found 7 papers, 1 papers with code
GGM knockoff filter: False Discovery Rate Control for Gaussian Graphical Models
We then estimate the neighborhood of each node, by comparing its feature statistics to its threshold, resulting in our graph estimate.
Methodology
Causal Structure Learning
Causal models can be viewed as a special class of graphical models that not only represent the distribution of the observed system but also the distributions under external interventions.
Methodology
Structure Learning in Graphical Modeling
A graphical model is a statistical model that is associated to a graph whose nodes correspond to variables of interest.
Distributional Equivalence and Structure Learning for Bow-free Acyclic Path Diagrams
We consider the problem of structure learning for bow-free acyclic path diagrams (BAPs).
A generalized back-door criterion
We generalize Pearl's back-door criterion for directed acyclic graphs (DAGs) to more general types of graphs that describe Markov equivalence classes of DAGs and/or allow for arbitrarily many hidden variables.
Order-independent constraint-based causal structure learning
This algorithm is known to be order-dependent, in the sense that the output can depend on the order in which the variables are given.
Learning high-dimensional directed acyclic graphs with latent and selection variables
However, we prove that any causal information in the output of RFCI is correct in the asymptotic limit.
