Machine Learning for Causal Inference
| 企業作者: | |
|---|---|
| 其他作者: | , |
| 總結: | XVI, 298 p. 73 illus., 49 illus. in color. text |
| 語言: | 英语 |
| 出版: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
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| 版: | 1st ed. 2023. |
| 主題: | |
| 在線閱讀: | https://doi.org/10.1007/978-3-031-35051-1 |
| 格式: | 電子 電子書 |
書本目錄:
- Overview of the Book
- Causal Inference Preliminary
- Causal Effect Estimation: Basic Methodologies
- Causal Inference on Graphs
- Causal Effect Estimation: Recent Progress, Challenges, and Opportunities
- Fair Machine Learning Through the Lens of Causality
- Causal Explainable AI
- Causal Domain Generalization
- Causal Inference and Natural Language Processing
- Causal Inference and Recommendations
- Causality Encourage the Identifiability of Instance-Dependent Label Noise
- Causal Interventional Time Series Forecasting on Multi-horizon and Multi-series Data
- Continual Causal Effect Estimation
- Summary.