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Found 7 papers, 0 papers with code
Scalable Structure Learning for Sparse Context-Specific Causal Systems
Several approaches to graphically representing context-specific relations among jointly distributed categorical variables have been proposed, along with structure learning algorithms.
Neuro-Causal Factor Analysis
Factor analysis (FA) is a statistical tool for studying how observed variables with some mutual dependences can be expressed as functions of mutually independent unobserved factors, and it is widely applied throughout the psychological, biological, and physical sciences.
Combinatorial and algebraic perspectives on the marginal independence structure of Bayesian networks
We consider the problem of estimating the marginal independence structure of a Bayesian network from observational data, learning an undirected graph we call the unconditional dependence graph.
A Transformational Characterization of Unconditionally Equivalent Bayesian Networks
We consider the problem of characterizing Bayesian networks up to unconditional equivalence, i. e., when directed acyclic graphs (DAGs) have the same set of unconditional $d$-separation statements.
Greedy Causal Discovery is Geometric
We show that the moves of the aforementioned algorithms are included within classes of edges of $\operatorname{CIM}_p$ and that restrictions placed on the skeleton of the candidate DAGs correspond to faces of $\operatorname{CIM}_p$.
Causal Discovery
Statistics Theory
Combinatorics
Statistics Theory
Representation of Context-Specific Causal Models with Observational and Interventional Data
We consider the problem of representing causal models that encode context-specific information for discrete data using a proper subclass of staged tree models which we call CStrees.
Permutation-based Causal Inference Algorithms with Interventions
Learning directed acyclic graphs using both observational and interventional data is now a fundamentally important problem due to recent technological developments in genomics that generate such single-cell gene expression data at a very large scale.
