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Abstract
This paper focuses on causal discovery, which aims at inferring the underlying causal relationships from observational samples. Existing methods of causal discovery rely on a large number of samples. So when the number of samples is limited, they often fail to produce correct causal graphs. To address this problem, we propose a novel framework: Firstly, given an expert-specified causal subgraph, we leverage contextual and statistical information of the variables to expand the subgraph with positive-unlabeled learning. Secondly, to ensure the faithfulness of the causal graph, with the expanded subgraph as the constraint, we resort to a structural equation model to discover the entire causal graph. Experimental results show that our method achieves significant improvement over the baselines, especially when only limited samples are given.
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Notes
- 1.
Other metric functions such as JS divergence, co-occurrence frequency can also be utilized to generate\(\mathbf {B}_{ij}\).
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Acknowledgement
This research was supported by the National Key Research and Development Project (No. 2020AAA0109302), National Natural Science Foundation of China (No. 62072323), Shanghai Science and Technology Innovation Action Plan (No. 19511120400) and Shanghai Municipal Science an Technology Major Project (No. 2021SHZDZX0103).
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Authors and Affiliations
Shanghai Key Lab. of Data Science, School of Computer Science, Fudan University, Shanghai, China
Xuwu Wang, Xueyao Jiang, Sihang Jiang, Zhixu Li & Yanghua Xiao
Fudan-Aishu Cognitive Intelligence Joint Research Center, Shanghai, China
Yanghua Xiao
- Xuwu Wang
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- Xueyao Jiang
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- Sihang Jiang
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- Zhixu Li
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- Yanghua Xiao
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Correspondence toYanghua Xiao.
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Editors and Affiliations
Indian Institute of Technology Kanpur, Kanpur, India
Arnab Bhattacharya
National University of Singapore, Singapore, Singapore
Janice Lee Mong Li
University of California, Santa Barbara, Santa Barbara, CA, USA
Divyakant Agrawal
IIIT Hyderabad, Hyderabad, India
P. Krishna Reddy
Indraprastha Institute of Information Technology Delhi, New Delhi, India
Mukesh Mohania
Ashoka University, Sonepat, Haryana, India
Anirban Mondal
Indraprastha Institute of Information Technology Delhi, New Delhi, India
Vikram Goyal
University of Aizu, Aizu, Japan
Rage Uday Kiran
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Wang, X., Jiang, X., Jiang, S., Li, Z., Xiao, Y. (2022). Utilizing Expert Knowledge and Contextual Information for Sample-Limited Causal Graph Construction. In: Bhattacharya, A.,et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13245. Springer, Cham. https://doi.org/10.1007/978-3-031-00123-9_46
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