Permutation-based Causal Inference Algorithms with Interventions
Permutation-based Causal Inference Algorithms with Interventions
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发表时间：
2017-05
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通讯作者：
Yuhao Wang;Liam Solus;Karren D. Yang;Caroline Uhler
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作者：
Yuhao Wang;Liam Solus;Karren D. Yang;Caroline Uhler
由于基因组学的最新技术发展可以大规模生成此类单细胞基因表达数据，因此使用观察数据和介入数据学习有向无环图现在已成为一个根本性的重要问题。为了利用这些数据来学习基因调控网络，需要有效且可靠的因果推理算法，该算法可以利用观察数据和干预数据。在本文中，我们提出了两种此类算法，并证明两者在忠实性假设下是一致的。这些算法是 Greedy SP 算法的介入改编，并且是第一个同时使用观察数据和介入数据并保证一致性的算法。此外，这些算法的优点是它们是非参数的，这使得它们对于分析非高斯数据也很有用。在本文中，我们介绍了这两种算法及其一致性保证，并分析了它们在模拟数据、蛋白质信号传导数据和单细胞基因表达数据上的性能。
Learning directed acyclic graphs using both observational and interventional data is now a fundamentally important problem due to recent technological developments in genomics that generate such single-cell gene expression data at a very large scale. In order to utilize this data for learning gene regulatory networks, efficient and reliable causal inference algorithms are needed that can make use of both observational and interventional data. In this paper, we present two algorithms of this type and prove that both are consistent under the faithfulness assumption. These algorithms are interventional adaptations of the Greedy SP algorithm and are the first algorithms using both observational and interventional data with consistency guarantees. Moreover, these algorithms have the advantage that they are nonparametric, which makes them useful also for analyzing non-Gaussian data. In this paper, we present these two algorithms and their consistency guarantees, and we analyze their performance on simulated data, protein signaling data, and single-cell gene expression data.