3 accepted papers
Learning graphical conditional independence structures is an important machine learning problem and a cornerstone of causal discovery. However, the accuracy and execution time of learning algorithms generally struggle to scale to problems with hundreds of highly connected variables---for instance, r…
There has been an increasing interest in methods that exploit permutation reasoning to search for directed acyclic causal models, including the “Ordering Search’’ of Teyssier and Kohler and GSP of Solus, Wang and Uhler. We extend the methods of the latter by a permutation-based operation tuck, and d…
The discovery of causal relationships is a core part of scientific research. Accordingly, over the past several decades, algorithms have been developed to discover the causal structure for a system of variables from observational data. Learning ancestral graphs is of particular interest due to their…