標(biāo)題: Titlebook: Machine Learning for Causal Inference; Sheng Li,Zhixuan Chu Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive lic [打印本頁(yè)] 作者: EXERT 時(shí)間: 2025-3-21 18:14
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
作者: deface 時(shí)間: 2025-3-21 20:52
https://doi.org/10.1007/978-3-031-35051-1Causality; Counterfactuals; Treatment Effect Estimation; Causal Discovery; statistics作者: 強(qiáng)壯 時(shí)間: 2025-3-22 02:09 作者: cunning 時(shí)間: 2025-3-22 08:36
http://image.papertrans.cn/m/image/620589.jpg作者: absolve 時(shí)間: 2025-3-22 09:10 作者: 傳授知識(shí) 時(shí)間: 2025-3-22 15:50
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.作者: d-limonene 時(shí)間: 2025-3-22 19:48
SummaryThis chapter summarizes this book and highlights research challenges and future opportunities on the topic of machine learning for causal inference.作者: 漂泊 時(shí)間: 2025-3-22 21:54 作者: ARIA 時(shí)間: 2025-3-23 04:06 作者: 獨(dú)輪車 時(shí)間: 2025-3-23 07:31
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作者: FLASK 時(shí)間: 2025-3-23 10:36
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).作者: reflection 時(shí)間: 2025-3-23 15:29 作者: 嗎啡 時(shí)間: 2025-3-23 21:41
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)).作者: muster 時(shí)間: 2025-3-23 23:02
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作者: chlorosis 時(shí)間: 2025-3-24 04:51 作者: esculent 時(shí)間: 2025-3-24 07:19 作者: 尊嚴(yán) 時(shí)間: 2025-3-24 14:24 作者: 持久 時(shí)間: 2025-3-24 15:10 作者: 高貴領(lǐng)導(dǎo) 時(shí)間: 2025-3-24 23:01 作者: fetter 時(shí)間: 2025-3-24 23:33
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作者: Dedication 時(shí)間: 2025-3-25 07:13
Book 2023anguage processing, recommender systems, computer vision, time series forecasting, and continual 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 m作者: Detonate 時(shí)間: 2025-3-25 08:14
d aussagekr?ftiges Marketing-Cockpit installieren m?chten oder punktuell nach dem Nutzenpotenzial einer bestimmten Kennzahl suchen. Studierende und Dozenten k?nnen einen fundierten Einblick in die praktische Ausgestaltung von Kennzahlen(-Systemen) gewinnen..978-3-540-79862-0作者: Scleroderma 時(shí)間: 2025-3-25 15:24 作者: diabetes 時(shí)間: 2025-3-25 18:43
Zhixuan Chu,Sheng Lid aussagekr?ftiges Marketing-Cockpit installieren m?chten oder punktuell nach dem Nutzenpotenzial einer bestimmten Kennzahl suchen. Studierende und Dozenten k?nnen einen fundierten Einblick in die praktische Ausgestaltung von Kennzahlen(-Systemen) gewinnen..978-3-540-79862-0作者: ADOPT 時(shí)間: 2025-3-26 00:01 作者: TIGER 時(shí)間: 2025-3-26 03:32 作者: 誰(shuí)在削木頭 時(shí)間: 2025-3-26 05:08 作者: 有害 時(shí)間: 2025-3-26 12:11
Jing Ma,Ruocheng Guo,Jundong Lid aussagekr?ftiges Marketing-Cockpit installieren m?chten oder punktuell nach dem Nutzenpotenzial einer bestimmten Kennzahl suchen. Studierende und Dozenten k?nnen einen fundierten Einblick in die praktische Ausgestaltung von Kennzahlen(-Systemen) gewinnen..978-3-540-79862-0作者: 煩憂 時(shí)間: 2025-3-26 14:55 作者: 意外 時(shí)間: 2025-3-26 18:39
d aussagekr?ftiges Marketing-Cockpit installieren m?chten oder punktuell nach dem Nutzenpotenzial einer bestimmten Kennzahl suchen. Studierende und Dozenten k?nnen einen fundierten Einblick in die praktische Ausgestaltung von Kennzahlen(-Systemen) gewinnen..978-3-540-79862-0作者: 值得 時(shí)間: 2025-3-26 21:59
Yongkai Wu,Lu Zhang,Xintao Wud aussagekr?ftiges Marketing-Cockpit installieren m?chten oder punktuell nach dem Nutzenpotenzial einer bestimmten Kennzahl suchen. Studierende und Dozenten k?nnen einen fundierten Einblick in die praktische Ausgestaltung von Kennzahlen(-Systemen) gewinnen..978-3-540-79862-0作者: TRUST 時(shí)間: 2025-3-27 01:57 作者: expire 時(shí)間: 2025-3-27 07:39 作者: 不成比例 時(shí)間: 2025-3-27 09:47 作者: 結(jié)合 時(shí)間: 2025-3-27 14:46
Wenqing Chen,Zhixuan Chud aussagekr?ftiges Marketing-Cockpit installieren m?chten oder punktuell nach dem Nutzenpotenzial einer bestimmten Kennzahl suchen. Studierende und Dozenten k?nnen einen fundierten Einblick in die praktische Ausgestaltung von Kennzahlen(-Systemen) gewinnen..978-3-540-79862-0作者: conference 時(shí)間: 2025-3-27 18:48 作者: SOBER 時(shí)間: 2025-3-28 01:16 作者: ovation 時(shí)間: 2025-3-28 04:12 作者: ATOPY 時(shí)間: 2025-3-28 07:43 作者: evaculate 時(shí)間: 2025-3-28 13:23
Causal Effect Estimation: Basic Methodologieswork, one of the well-known causal inference frameworks. The methods are divided into two categories depending on whether they require all three assumptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced作者: NAUT 時(shí)間: 2025-3-28 15:58
Causal Inference on Graphsct fields, such as social network analysis, bioinformatics, crime forecasting, economics, and recommender systems. Different from most traditional causal inference studies, which focus on independent and identically distributed (i.i.d.) data, causal inference on graphs has recently attracted increas作者: DEBT 時(shí)間: 2025-3-28 19:28 作者: 安撫 時(shí)間: 2025-3-29 00:24
Fair Machine Learning Through the Lens of Causality09) Causality. Cambridge University Press), this framework defines fairness in the categories of direct/indirect discrimination, system/group/individual-level discrimination, and their derivatives, e.g., indirect individual-level discrimination. The framework can unify various causal fairness notion作者: 危險(xiǎn) 時(shí)間: 2025-3-29 05:31
Causal Explainable AIance measurements such as accuracy. However, as machine learning techniques have been applied to fields that are highly sensitive to risk, such as healthcare, law enforcement, and finance, the trustworthiness of models, especially their explainability, has become an increasingly important concern. F作者: Cubicle 時(shí)間: 2025-3-29 08:19
Causal Domain Generalization. assumption, independent and identically distributed assumption, states that the training and test data are sampled from the same distribution. On the other hand, real-world scenarios are more dynamic, with training and test data not always coming from the same distribution. In such cases, models b作者: 一再困擾 時(shí)間: 2025-3-29 12:14
Causal Inference and Natural Language Processingl questions: (1) how can NLP aid in causal inference when working with textual data, and (2) how can causal inference theory enhance the robustness and interpretability of NLP models? We present the latest developments and challenges in each area. Firstly, we discuss the difficulties associated with作者: 引導(dǎo) 時(shí)間: 2025-3-29 19:19 作者: 注意到 時(shí)間: 2025-3-29 23:31
