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Heteroscedastic Causal Structure Learning

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posted on 2023-11-21, 03:56 authored by B Duong, Thin NguyenThin Nguyen
Heretofore, learning the directed acyclic graphs (DAGs) that encode the cause-effect relationships embedded in observational data is a computationally challenging problem. A recent trend of studies has shown that it is possible to recover the DAGs with polynomial time complexity under the equal variances assumption. However, this prohibits the heteroscedasticity of the noise, which allows for more flexible modeling capabilities, but at the same time is substantially more challenging to handle. In this study, we tackle the heteroscedastic causal structure learning problem under Gaussian noises. By exploiting the normality of the causal mechanisms, we can recover a valid causal ordering, which can uniquely identify the causal DAG using a series of conditional independence tests. The result is HOST (Heteroscedastic causal STructure learning), a simple yet effective causal structure learning algorithm that scales polynomially in both sample size and dimensionality. In addition, via extensive empirical evaluations on a wide range of both controlled and real datasets, we show that the proposed HOST method is competitive with state-of-the-art approaches in both the causal order learning and structure learning problems.

History

Volume

372

Pagination

598-605

ISSN

0922-6389

eISSN

1879-8314

ISBN-13

9781643684369

Language

eng

Publisher

IOS Press

Place of publication

Amsterdam, The Netherlands

Title of book

Frontiers in Artificial Intelligence and Applications

Series

Frontiers in Artificial Intelligence and Applications

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