Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow Shrink Trees

Bryan Andrews, Joseph Ramsey, Ruben Sanchez Romero, Jazmin Camchong, Erich Kummerfeld

Keywords: Causal Discovery, Directed Acyclic Graphs, DAGs, fMRI, Graphical Models, High Dimension, Densely Connected

TL;DR: We present new methods for causal discovery that are fast scalable and accurate. These methods are validated in many conditions including fMRI data.

Abstract: 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, recovering brain networks from fMRI data. We introduce the best order score search (BOSS) and grow-shrink trees (GSTs) for learning directed acyclic graphs (DAGs) in this paradigm. BOSS greedily searches over permutations of variables, using GSTs to construct and score DAGs from permutations. GSTs efficiently cache scores to eliminate redundant calculations. BOSS achieves state-of-the-art performance in accuracy and execution time, comparing favorably to a variety of combinatorial and gradient-based learning algorithms under a broad range of conditions. To demonstrate its practicality, we apply BOSS to two sets of resting-state fMRI data: simulated data with pseudo-empirical noise distributions derived from randomized empirical fMRI cortical signals and clinical data from 3T fMRI scans processed into cortical parcels. BOSS is available for use within the TETRAD project which includes Python and R wrappers.

Submission Number: 14386