Optimizing Causal Orderings for Generating DAGs from Data

03/13/2013
by   Remco R. Bouckaert, et al.
0

An algorithm for generating the structure of a directed acyclic graph from data using the notion of causal input lists is presented. The algorithm manipulates the ordering of the variables with operations which very much resemble arc reversal. Operations are only applied if the DAG after the operation represents at least the independencies represented by the DAG before the operation until no more arcs can be removed from the DAG. The resulting DAG is a minimal l-map.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/17/2023

Generating Bayesian Network Models from Data Using Tsetlin Machines

Bayesian networks (BN) are directed acyclic graphical (DAG) models that ...
research
03/13/2013

An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation

In a previous paper [Pearl and Verma, 1991] we presented an algorithm fo...
research
08/09/2014

A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model

Structural equation models and Bayesian networks have been widely used t...
research
02/28/2023

Practical Algorithms for Orientations of Partially Directed Graphical Models

In observational studies, the true causal model is typically unknown and...
research
06/11/2022

Greedy Relaxations of the Sparsest Permutation Algorithm

There has been an increasing interest in methods that exploit permutatio...
research
03/06/2013

Discounting and Combination Operations in Evidential Reasoning

Evidential reasoning is now a leading topic in Artificial Intelligence. ...
research
08/24/2015

A note on the complexity of the causal ordering problem

In this note we provide a concise report on the complexity of the causal...

Please sign up or login with your details

Forgot password? Click here to reset