Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference-Reference-Cited by-同舟云学术

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Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference

Published:2023-12-01 Issue: Volume:18 Page:51-60
ISSN:
Container-title:2023 IEEE International Conference on Data Mining (ICDM)
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Author:

Cheng Debo¹,Xie Yang²,Xu Ziqi¹,Li Jiuyong¹,Liu Lin¹,Liu Jixue¹,Zhang Yinghao²,Feng Zaiwen²

Affiliation:

1. University of South Australia,UniSA STEM,Adelaide,Australia

2. Huazhong Agricultural University,College of Informatics,Wuhan,China

Funder

Australian Research Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10415628/10415561/10415730.pdf?arnumber=10415730

Reference55 articles.

1. Causal Inference for Statistics, Social, and Biomedical Sciences

2. Causal Inference Using Potential Outcomes

3. Quantifying Biases in Causal Models: Classical Confounding vs Collider-Stratification Bias

4. The Effectiveness of Right Heart Catheterization in the Initial Care of Critically III Patients

5. Causality

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