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Machine Learning for Causal Inference

Overview of attention for book
Cover of 'Machine Learning for Causal Inference'

Table of Contents

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    Book Overview
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    Chapter 1 Overview of the Book
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    Chapter 2 Causal Inference Preliminary
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    Chapter 3 Causal Effect Estimation: Basic Methodologies
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    Chapter 4 Causal Inference on Graphs
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    Chapter 5 Causal Effect Estimation: Recent Progress, Challenges, and Opportunities
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    Chapter 6 Fair Machine Learning Through the Lens of Causality
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    Chapter 7 Causal Explainable AI
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    Chapter 8 Causal Domain Generalization
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    Chapter 9 Causal Inference and Natural Language Processing
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    Chapter 10 Causal Inference and Recommendations
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    Chapter 11 Causality Encourages the Identifiability of Instance-Dependent Label Noise
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    Chapter 12 Causal Interventional Time Series Forecasting on Multi-horizon and Multi-series Data
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    Chapter 13 Continual Causal Effect Estimation
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    Chapter 14 Summary
Attention for Chapter 9: Causal Inference and Natural Language Processing
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Chapter title
Causal Inference and Natural Language Processing
Chapter number 9
Book title
Machine Learning for Causal Inference
Published by
Springer, Cham, January 2023
DOI 10.1007/978-3-031-35051-1_9
Book ISBNs
978-3-03-135050-4, 978-3-03-135051-1
Authors

Chen, Wenqing, Chu, Zhixuan

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 100%
Readers by discipline Count As %
Business, Management and Accounting 2 100%

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