Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees
This addresses the problem of scalable and accurate causal discovery for researchers and practitioners dealing with complex datasets like fMRI, though it appears incremental as it builds on existing permutation-based methods with efficiency improvements.
The paper tackles the challenge of learning directed acyclic graphs (DAGs) for causal discovery, which struggles with accuracy and speed for hundreds of highly connected variables, such as in brain network recovery from fMRI data. It introduces BOSS and GSTs, achieving state-of-the-art performance in both accuracy and execution time compared to various existing algorithms.
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.