Discovering Ancestral Instrumental Variables for Causal Inference From Observational Data-Reference-Cited by-同舟云学术

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Discovering Ancestral Instrumental Variables for Causal Inference From Observational Data

Published:2023 Issue: Volume: Page:1-11
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Cheng Debo¹^ORCID,Li Jiuyong²^ORCID,Liu Lin²^ORCID,Yu Kui³^ORCID,Le Thuc Duy²^ORCID,Liu Jixue²^ORCID

Affiliation:

1. Guangxi Key Laboratory of Big Data in Finance and Economics, Guangxi University of Finance and Economics, Nanning, China

2. STEM, University of South Australia, Mawson Lakes, SA, Australia

3. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China

Funder

Australian Research Council

National Key Research and Development Program of China

Guangxi Key Laboratory of Big Data in Finance and Economics

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/10092521.pdf?arnumber=10092521

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1. Causal inference in the medical domain: a survey;Applied Intelligence;2024-03

2. Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey;ACM Computing Surveys;2024-01-12

3. Testing Conditional Independence Between Latent Variables by Independence Residuals;IEEE Transactions on Neural Networks and Learning Systems;2024

4. Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference;2023 IEEE International Conference on Data Mining (ICDM);2023-12-01

5. LRAGAD: Local Information Recognition for Attribute Graph Anomaly Detection;2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI);2023-11-06

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