Essays On Using Machine Learning For Causal Inference In Social Science
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Essays On Using Machine Learning For Causal Inference In Social Science
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Author : Jonathan Bernhard Fuhr
language : en
Publisher:
Release Date : 2024
Essays On Using Machine Learning For Causal Inference In Social Science written by Jonathan Bernhard Fuhr and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.
Machine Learning For Causal Inference
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Author : Sheng Li
language : en
Publisher: Springer Nature
Release Date : 2023-11-25
Machine Learning For Causal Inference written by Sheng Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-25 with Computers categories.
This 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, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language 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 machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.
Proceedings Of The International Conference On Intelligent Computing Communication And Information Security
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Author : Vladan Devedzic
language : en
Publisher: Springer Nature
Release Date : 2023-07-03
Proceedings Of The International Conference On Intelligent Computing Communication And Information Security written by Vladan Devedzic and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-03 with Technology & Engineering categories.
This book contains high quality research papers accepted and presented at the International Conference on Intelligent Computing, Communication and Information Security (ICICCIS 2022), organized by Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), Jaipur, India during 25-26, November 2022. It presents the solutions of issues and challenges in intelligent computing, communication and information security domains. This book provides a background to problem domains, considering the progress so far, assessing the potential of such approaches, and exploring possible future directions as a single readily accessible source.
Essays In Honor Of Cheng Hsiao
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Author : Dek Terrell
language : en
Publisher: Emerald Group Publishing
Release Date : 2020-04-15
Essays In Honor Of Cheng Hsiao written by Dek Terrell and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-15 with Business & Economics categories.
Including contributions spanning a variety of theoretical and applied topics in econometrics, this volume of Advances in Econometrics is published in honour of Cheng Hsiao.
Handbook Of Computational Social Science Volume 1
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Author : Uwe Engel
language : en
Publisher: Routledge
Release Date : 2021-11-10
Handbook Of Computational Social Science Volume 1 written by Uwe Engel and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-10 with Computers categories.
The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field but also encourages growth in new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientifi c and engineering sectors.
Sociological Foundations Of Computational Social Science
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Author : Yoshimichi Sato
language : en
Publisher: Springer Nature
Release Date : 2024-03-23
Sociological Foundations Of Computational Social Science written by Yoshimichi Sato and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-23 with Social Science categories.
This book provides solid sociological foundations to computational social science (CSS). CSS is an emerging research field, and many books with those words in the title are on the market. However, CSS has not become mainstream in sociology, for which there are two reasons. First, CSS does not necessarily solve major research questions in sociology. Second, its sociological foundations are weak. These two reasons are interrelated—that is, CSS cannot solve major research questions because its sociological foundations are weak. Thus, even if it tries to solve those questions, its approaches seem to mainstream sociologists to miss the point. To resolve that shortcoming, this book fills the gap between CSS and sociology, shows that CSS can solve major research questions in sociology, and advances sociology by introducing to it theories and methodologies of CSS.
The Oxford Handbook Of The Sociology Of Machine Learning
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Author : Juan Pablo Pardo-Guerra
language : en
Publisher: Oxford University Press
Release Date : 2025-04
The Oxford Handbook Of The Sociology Of Machine Learning written by Juan Pablo Pardo-Guerra and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04 with Social Science categories.
Machine learning, renowned for its ability to detect patterns in large datasets, has seen a significant increase in applications and complexity since the early 2000s. The Oxford Handbook of the Sociology of Machine Learning offers a state-of-the-art and forward-looking overview of the intersection between machine learning and sociology, exploring what sociology can gain from machine learning and how it can shed new light on the societal implications of this technology. Through its 39 chapters, an international group of sociologists address three key questions. First, what can sociologists yield from using machine learning as a methodological tool? This question is examined across various data types, including text, images, and sound, with insights into how machine learning and ethnography can be combined. Second, how is machine learning being used throughout society, and what are its consequences? The Handbook explores this question by examining the assumptions and infrastructures behind machine learning applications, as well as the biases they might perpetuate. Themes include art, cities, expertise, financial markets, gender, race, intersectionality, law enforcement, medicine, and the environment, covering contexts across the Global South and Global North. Third, what does machine learning mean for sociological theory and theorizing? Chapters examine this question through discussions on agency, culture, human-machine interaction, influence, meaning, power dynamics, prediction, and postcolonial perspectives. The Oxford Handbook of the Sociology of Machine Learning is an essential resource for academics and students interested in artificial intelligence, computational social science, and the role and implications of machine learning in society.
Making It Count
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Author : Arunabh Ghosh
language : en
Publisher: Princeton University Press
Release Date : 2022-01-11
Making It Count written by Arunabh Ghosh and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-11 with History categories.
"Among the biggest challenges facing leaders of the newly established People's Republic of China (PRC) was how much they did not know. In 1949, at the end of a long sequence of wars, the government of one of the largest states in the world committed to fundamentally re-engineering its society and economy via socialist planning while having almost no hard, reliable statistical data about their own country. This book is a history of attempts made to resolve this "crisis in counting." Drawing on a wealth of official, institutional, and private sources culled from China, India, and the United States, the author explores the choices made and the effects they engendered through a series of vivid encounters with political leaders, professional statisticians, academics, ordinary statistical workers, and even literary figures. Early reliance on Soviet-inspired methods of enumeration became increasingly untenable in China by the middle of the 1950s. A series of unprecedented and unexpected exchanges with Indian statisticians followed, as the Chinese sought to learn about the then exciting new technology of random sampling. These developments were, in turn, overtaken by the tumult of the Great Leap Forward (1958-1961), when both probabilistic and exhaustive methods were rejected and statistics was refashioned into an essentially ethnographic enterprise. The author argues that this history, usually narrowly described as a universal, if European history, cannot be understood without acknowledging Soviet and Indian influences which not only revises existing models of Cold War science but also globalizes the wider developments in the history of statistics and data. For historians of China and social science, and political scientists, sociologists, and anthropologists studying modern China"--
The Routledge Handbook Of Causality And Causal Methods
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Author : Phyllis Illari
language : en
Publisher: Taylor & Francis
Release Date : 2024-12-30
The Routledge Handbook Of Causality And Causal Methods written by Phyllis Illari and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-30 with Philosophy categories.
The Routledge Handbook of Causality and Causal Methods adopts a pluralistic, interdisciplinary approach to causality. It formulates distinct questions and problems of causality as they arise across scientific and policy fields. Exploring, in a comparative way, how these questions and problems are addressed in different areas, the Handbook fosters dialogue and exchange. It emphasizes the role of the researchers and the normative considerations that arise in the development of methodological and empirical approaches. The Handbook includes authors from all over the world and with many different disciplinary backgrounds, and its 50 chapters appear in print here for the first time. The chapters are organized into the following seven parts: Causal Pluralism from Theory to Practice Causal Theory and the Role of Researchers Features of Causal Systems Causal Methods, Experimentation and Observation Measurement and Data Causality, Knowledge, and Action Causal Theory across Disciplinary Borders Essential reading for scholars interested in an interdisciplinary approach to causality and causal methods, the volume is also a valuable resource for advanced undergraduates as well as for graduate students interested in delving into the rich field of causality. Chapters 15 and 36 of this book are freely available as downloadable Open Access PDFs at http://www.taylorfrancis.com under a Creative Commons [Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND)] 4.0 license.
Handbook Of Causal Analysis For Social Research
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Author : Stephen L. Morgan
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-22
Handbook Of Causal Analysis For Social Research written by Stephen L. Morgan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-22 with Social Science categories.
What constitutes a causal explanation, and must an explanation be causal? What warrants a causal inference, as opposed to a descriptive regularity? What techniques are available to detect when causal effects are present, and when can these techniques be used to identify the relative importance of these effects? What complications do the interactions of individuals create for these techniques? When can mixed methods of analysis be used to deepen causal accounts? Must causal claims include generative mechanisms, and how effective are empirical methods designed to discover them? The Handbook of Causal Analysis for Social Research tackles these questions with nineteen chapters from leading scholars in sociology, statistics, public health, computer science, and human development.