ACE: Adaptively Similarity-Preserved Representation Learning for Individual Treatment Effect Estimation-Reference-Cited by-同舟云学术

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ACE: Adaptively Similarity-Preserved Representation Learning for Individual Treatment Effect Estimation

Published:2019-11 Issue: Volume: Page:
ISSN:
Container-title:2019 IEEE International Conference on Data Mining (ICDM)
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Author:

Yao Liuyi,Li Sheng,Li Yaliang,Huai Mengdi,Gao Jing,Zhang Aidong

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8961330/8970627/08970934.pdf?arnumber=8970934

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. CE-RCFR: Robust Counterfactual Regression for Consensus-Enabled Treatment Effect Estimation;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

3. Mitigating selection bias in counterfactual prediction through self-supervised domain embedding learning with virtual samples;Applied Intelligence;2024-04

4. Deep causal learning for robotic intelligence;Frontiers in Neurorobotics;2023-02-22

5. Causal Effect Estimation: Basic Methodologies;Machine Learning for Causal Inference;2023

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