Sequentially learning the topological ordering of directed acyclic graphs with likelihood ratio scores

Published: 22 Dec 2022, Last Modified: 28 Feb 2023Accepted by TMLREveryoneRevisionsBibTeX
Abstract: Causal discovery, the learning of causality in a data mining scenario, has been of strong scientific and theoretical interest as a starting point to identify "what causes what?'' Contingent on assumptions and a proper learning algorithm, it is sometimes possible to identify and accurately estimate an underlying directed acyclic graph (DAG), as opposed to a Markov equivalence class of graphs that gives ambiguity of causal directions. The focus of this paper is in highlighting the identifiability and estimation of DAGs through a sequential sorting procedure that orders variables one at a time, starting at root nodes, followed by children of the root nodes, and so on until completion. We demonstrate a novel application of this general sequential approach to estimate the topological ordering of the DAG corresponding to a linear structural equation model with a non-Gaussian error distribution family. At each step of the procedure, only simple likelihood ratio scores are calculated on regression residuals to decide the next node to append to the current partial ordering. The computational complexity of our algorithm on a $p$-node problem is $\mathcal{O}(pd)$, where $d$ is the maximum neighborhood size. Under mild assumptions, the population version of our procedure provably identifies a true ordering of the underlying DAG. We provide extensive numerical evidence to demonstrate that this sequential procedure scales to possibly thousands of nodes and works well for high-dimensional data. We accompany these numerical experiments with an application to a single-cell gene expression dataset. Our $\texttt{R}$ package with examples and installation instructions can be found at
Submission Length: Long submission (more than 12 pages of main content)
Changes Since Last Submission: 1. Added $\texttt{R}$ package installation link to abstract. 2. In abstract, made more clear the contribution of the paper is for a linear SEM with non-Gaussian noise. 3. We added more citations at the end of paragraph $1$ in the introduction. 4. Fixed minor typos. 5. In accordance with one reviewer's suggestion: in Appendix B.3, we present results for the case that the data matrix is not re-scaled (unlike Section 3.1). Next, Appendix B.4 obtains results for the case that neighborhood sets must be estimated. Section Section 3.1 of the main text now refers to these appendices.
Assigned Action Editor: ~Patrick_Flaherty1
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Submission Number: 313