Optimizing Causal Orderings for Generating DAGs from Data

TL;DR

This paper introduces an algorithm that optimizes variable orderings to generate minimal DAGs from data, ensuring the resulting structure captures essential independencies while minimizing complexity.

Contribution

It presents a novel algorithm that manipulates variable orderings using arc reversal-like operations to produce minimal l-maps from data.

Findings

The algorithm effectively generates minimal DAGs from data.

It preserves essential independencies while reducing unnecessary arcs.

The method ensures the resulting DAG is a minimal l-map.

Abstract

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.