Summary of Doubly Robust Causal Effect Estimation Under Networked Interference Via Targeted Learning, by Weilin Chen et al.
Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning
by Weilin Chen, Ruichu Cai, Zeqin Yang, Jie Qiao, Yuguang Yan, Zijian Li, Zhifeng Hao
First submitted to arxiv on: 6 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel doubly robust causal effect estimator under networked interference proposes adapting targeted learning technique to train neural networks, generalizing it for networked interference settings and establishing conditions for double robustness. The designed end-to-end estimator is analyzed theoretically, showing a faster convergence rate than single nuisance models. Experimental results on two real-world networks with semisynthetic data demonstrate the effectiveness of the proposed estimators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to estimate how things are connected in complex networks has been developed. This method helps reduce errors caused by incorrectly assuming certain patterns exist in the network. It uses a type of neural network and combines it with another technique called targeted learning. The result is an improved estimator that can accurately measure the connections between things in a network. |
Keywords
» Artificial intelligence » Neural network
