Reliable Causal Discovery with Improved Exact Search and Weaker AssumptionsDownload PDF

Published: 09 Nov 2021, Last Modified: 25 Nov 2024NeurIPS 2021 PosterReaders: Everyone
Keywords: Causal discovery, exact score-based method, weaker assumptions, scalability
Abstract: Many of the causal discovery methods rely on the faithfulness assumption to guarantee asymptotic correctness. However, the assumption can be approximately violated in many ways, leading to sub-optimal solutions. Although there is a line of research in Bayesian network structure learning that focuses on weakening the assumption, such as exact search methods with well-defined score functions, they do not scale well to large graphs. In this work, we introduce several strategies to improve the scalability of exact score-based methods in the linear Gaussian setting. In particular, we develop a super-structure estimation method based on the support of inverse covariance matrix which requires assumptions that are strictly weaker than faithfulness, and apply it to restrict the search space of exact search. We also propose a local search strategy that performs exact search on the local clusters formed by each variable and its neighbors within two hops in the super-structure. Numerical experiments validate the efficacy of the proposed procedure, and demonstrate that it scales up to hundreds of nodes with a high accuracy.
Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
TL;DR: We develop methods to estimate super-structure with weaker assumptions than faithfulness and to scale up exact score-based search.
Supplementary Material: pdf
Code: https://github.com/ignavierng/local-astar
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/reliable-causal-discovery-with-improved-exact/code)
12 Replies

Loading