On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Models

ArXi:2502.08531v3 Announce Type: replace Conditional-independence-based discovery uses statistical tests to identify a graphical model that represents the independence structure of variables in a dataset. These tests, however, can be unreliable, and algorithms are sensitive to errors and violated assumptions. Often, there are tests that were not used in the construction of the graph. In this work, we show that these redundant tests have the potential to detect or sometimes correct errors in the learned model.