Optimizing Causal Orderings for Generating DAGs from Data
This work addresses the challenge of causal structure learning for researchers in statistics and machine learning, but it appears incremental as it builds on existing concepts like arc reversal and minimal l-maps.
The paper tackles the problem of generating directed acyclic graphs (DAGs) from data by optimizing causal orderings, resulting in a minimal l-map that represents at least the independencies of the original DAG.
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.