An efficient algorithm for large-scale causal discovery

Hong, Yinghan; Liu, Zhusong; Mai, Guizhen

doi:10.1007/s00500-016-2281-0

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…With hundreds of time-series (or hundreds of static nodes), the potential possibility for causal relations grows exponentially. Existing approaches may fail because they involve either massive conditional independence tests (Runge et al 2019), too many variables to be conditioned on (Hong, Liu, and Mai 2017), or large quantities of parameters to be optimized (Tank et al 2022;Cheng et al 2023). To solve this problem, scalable or high-dimensional causal discovery approaches are proposed.…”

Section: Related Workmentioning

confidence: 99%

“…To solve this problem, scalable or high-dimensional causal discovery approaches are proposed. In static settings, Hong, Liu, and Mai (2017) and Morales-Alvarez et al (2022) propose to boost scalability via divide-and-conquer technique, Lopez et al (2022) limit the search space to low-rank factor graphs, Cundy, Grover, and Ermon (2021) instead leverages variational framework. In time-series settings like ours, the scalability issue is less explored.…”

Section: Related Workmentioning

confidence: 99%

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CUTS+: High-Dimensional Causal Discovery from Irregular Time-Series

Cheng,

Li,

Xiao

et al. 2024

AAAI

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Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios. Recently, researchers successfully discover causality by combining neural networks with Granger causality, but their performances degrade largely when encountering high-dimensional data because of the highly redundant network design and huge causal graphs. Moreover, the missing entries in the observations further hamper the causal structural learning. To overcome these limitations, We propose CUTS+, which is built on the Granger-causality-based causal discovery method CUTS and raises the scalability by introducing a technique called Coarse-to-fine-discovery (C2FD) and leveraging a message-passing-based graph neural network (MPGNN). Compared to previous methods on simulated, quasi-real, and real datasets, we show that CUTS+ largely improves the causal discovery performance on high-dimensional data with different types of irregular sampling.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

CUTS+: High-Dimensional Causal Discovery from Irregular Time-Series

Cheng,

Li,

Xiao

et al. 2024

AAAI

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“…Theorem 1 [13] Two DAGs with the same node set are said to Markov equivalent if they contain the same set of vstructures.…”

Section: Bayesian Networkmentioning

confidence: 99%

A decomposition structure learning algorithm in Bayesian network based on a two-stage combination method

Guo

2022

Complex Intell. Syst.

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Decomposition hybrid algorithms with the recursive framework which recursively decompose the structural task into structural subtasks to reduce computational complexity are employed to learn Bayesian network (BN) structure. Merging rules are commonly adopted as the combination method in the combination step. The direction determination rule of merging rules has problems in using the idea of keeping v-structures unchanged before and after combination to determine directions of edges in the whole structure. It breaks down in one case due to appearances of wrong v-structures, and is hard to operate in practice. Therefore, we adopt a novel approach for direction determination and propose a two-stage combination method. In the first-stage combination method, we determine nodes, links of edges by merging rules and adopt the idea of permutation and combination to determine directions of contradictory edges. In the second-stage combination method, we restrict edges between nodes that do not satisfy the decomposition property and their parent nodes by determining the target domain according to the decomposition property. Simulation experiments on four networks show that the proposed algorithm can obtain BN structure with higher accuracy compared with other algorithms. Finally, the proposed algorithm is applied to the thickening process of gold hydrometallurgy to solve the practical problem.

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“…Zhou et al 11 proposed a causal discovery algorithm to discover causal rules in large databases. Hong et al 12 proposed the LSCD framework, which uses a graph‐partitioning method and infer the causality of each subdataset. Ma et al 13 proposed two efficient algorithms to mine the combined causes from large data sets.…”

Section: Introductionmentioning

confidence: 99%

Bayesian network structure learning with improved genetic algorithm

Sun

Zhou

2022

Int J of Intelligent Sys

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As an important model of machine learning, Bayesian networks (BNs) have received a lot of attentions since they can be used for classification via probabilistic inference. However, since it is a complicated combination optimization problem, BN structure learning cannot be solved with classic convex optimization algorithms. Hence, evolutionary algorithms provide an alternative way to find a global solution to BN structure learning problem. In this paper, we improve the biased random‐key genetic algorithm to solve the BN structure learning problem. Meanwhile, we apply a local optimization model as its decoder to improve the performance of the proposed algorithm. Finally, we conduct our experiments on nine benchmark networks and a real dataset of cross‐site scripting (XSS) attack. Experimental results show that the proposed algorithm can obtain more accurate solutions than other state‐of‐the‐art algorithms and achieve a good performance in XSS attack detection for web security.

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An efficient algorithm for large-scale causal discovery

Cited by 6 publications

References 19 publications

CUTS+: High-Dimensional Causal Discovery from Irregular Time-Series

CUTS+: High-Dimensional Causal Discovery from Irregular Time-Series

A decomposition structure learning algorithm in Bayesian network based on a two-stage combination method

Bayesian network structure learning with improved genetic algorithm

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