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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書目名稱Machine Learning for Causal Inference影響因子(影響力)




書目名稱Machine Learning for Causal Inference影響因子(影響力)學(xué)科排名




書目名稱Machine Learning for Causal Inference網(wǎng)絡(luò)公開度




書目名稱Machine Learning for Causal Inference網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning for Causal Inference被引頻次




書目名稱Machine Learning for Causal Inference被引頻次學(xué)科排名




書目名稱Machine Learning for Causal Inference年度引用




書目名稱Machine Learning for Causal Inference年度引用學(xué)科排名




書目名稱Machine Learning for Causal Inference讀者反饋




書目名稱Machine Learning for Causal Inference讀者反饋學(xué)科排名

deface

https://doi.org/10.1007/978-3-031-35051-1Causality; Counterfactuals; Treatment Effect Estimation; Causal Discovery; statistics

強(qiáng)壯

cunning

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absolve

傳授知識(shí)

Overview of the BookThis chapter briefly introduces the general concepts of machine learning and causal inference, discusses their connections, and then presents the organization of this book. The research topic of each chapter is also briefly described to serve as a road map of the book.

d-limonene

SummaryThis chapter summarizes this book and highlights research challenges and future opportunities on the topic of machine learning for causal inference.

漂泊

ARIA

獨(dú)輪車

and interpretability challenges posed by conventional ML metThis book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, il