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- W3000587186 abstract "Abstract Recovering causal relationships from observed data is crucial to a variety of applications. Due to the curse of dimensionality, general causal discovery methods such as constraint-based methods and functional model based methods are not quite effective and efficient for large and high-dimensional data sets. Thus, some causal partitioning methods have been proposed to handle this problem. However, existing causal partitioning methods rely on high-order conditional independent (CI) tests, which makes them inefficient in handling dense causal graphs. Therefore, high-dimensionality is still a big challenge to these methods. In this work, we propose a new split-and-merge strategy to enable effective and scalable causality discovery. Different from the existing methods, our method uses only low-order CI tests, can get more accurate results and is applicable to various scenarios. We provide both theoretic analysis and empirical evaluation on the proposed method. Experiments on various real-world causal graphs show that the proposed method outperforms the stat-of-the-art method in terms of accuracy, efficiency and scalability. For high-dimensional cases, our method is much faster than the counterpart by one to three orders of magnitudes." @default.
- W3000587186 created "2020-01-23" @default.
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- W3000587186 date "2020-05-01" @default.
- W3000587186 modified "2023-09-24" @default.
- W3000587186 title "Effective and scalable causal partitioning based on low-order conditional independent tests" @default.
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- W3000587186 doi "https://doi.org/10.1016/j.neucom.2020.01.021" @default.
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