Titlebook: Machine Learning for Causal Inference; Sheng Li,Zhixuan Chu Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive lic

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Causal Explainable AIther improve the interpretability of machine learning models, some recent works in explainability have attempted to use causal reasoning techniques. In this chapter, we aim to provide an overview of causal explanation and discuss the design of . (CXAI).

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Causal Effect Estimation: Basic Methodologiesptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared. Most contents in this chapter are reprinted from our work (Yao et al. (ACM Trans Knowl Discov Data 15(5):1–46, 2021)).

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tinual learning. Each chapter of the book is written by leading researchers in their respective fields...Machine Learning for Causal Inference. explores the challenges associated with the relationship between m978-3-031-35053-5978-3-031-35051-1

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Causal Inference and Recommendationshelp readers gain a comprehensive understanding of this promising area. We start with the basic concepts of traditional RSs and their limitations due to the lack of causal reasoning ability. We then discuss how different causal inference techniques can be introduced to address these challenges, with