Download or read book An Introduction to Causal Inference written by Judea Pearl and published by Createspace Independent Publishing Platform. This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called "causal effects" or "policy evaluation") (2) queries about probabilities of counterfactuals, (including assessment of "regret," "attribution" or "causes of effects") and (3) queries about direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. -- p. 1.

Download or read book Essays in Honor of Cheng Hsiao written by Dek Terrell and published by Emerald Group Publishing. This book was released on 2020-04-15 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Machine Learning for Causal Inference written by Sheng Li and published by Springer Nature. This book was released on 2023-11-25 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Elements of Causal Inference written by Jonas Peters and published by MIT Press. This book was released on 2017-11-29 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

Download or read book The Book of Why written by Judea Pearl and published by Basic Books. This book was released on 2018-05-15 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence "Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.

Download or read book Causal Inference in Econometrics written by Van-Nam Huynh and published by Springer. This book was released on 2015-12-28 with total page 626 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Download or read book Causal Inference in Statistics written by Judea Pearl and published by John Wiley & Sons. This book was released on 2016-01-25 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.

Download or read book Causal Inference and Discovery in Python written by Aleksander Molak and published by Packt Publishing Ltd. This book was released on 2023-05-31 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data Purchase of the print or Kindle book includes a free PDF eBook Key Features Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more Discover modern causal inference techniques for average and heterogenous treatment effect estimation Explore and leverage traditional and modern causal discovery methods Book DescriptionCausal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more. By the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.What you will learn Master the fundamental concepts of causal inference Decipher the mysteries of structural causal models Unleash the power of the 4-step causal inference process in Python Explore advanced uplift modeling techniques Unlock the secrets of modern causal discovery using Python Use causal inference for social impact and community benefit Who this book is for This book is for machine learning engineers, researchers, and data scientists looking to extend their toolkit and explore causal machine learning. It will also help people who’ve worked with causality using other programming languages and now want to switch to Python, those who worked with traditional causal inference and want to learn about causal machine learning, and tech-savvy entrepreneurs who want to go beyond the limitations of traditional ML. You are expected to have basic knowledge of Python and Python scientific libraries along with knowledge of basic probability and statistics.

Download or read book Proceedings of the International Conference on Intelligent Computing Communication and Information Security written by Vladan Devedzic and published by Springer Nature. This book was released on 2023-07-03 with total page 467 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Possible Minds written by John Brockman and published by Penguin. This book was released on 2019-02-19 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Science world luminary John Brockman assembles twenty-five of the most important scientific minds, people who have been thinking about the field artificial intelligence for most of their careers, for an unparalleled round-table examination about mind, thinking, intelligence and what it means to be human. "Artificial intelligence is today's story--the story behind all other stories. It is the Second Coming and the Apocalypse at the same time: Good AI versus evil AI." --John Brockman More than sixty years ago, mathematician-philosopher Norbert Wiener published a book on the place of machines in society that ended with a warning: "we shall never receive the right answers to our questions unless we ask the right questions.... The hour is very late, and the choice of good and evil knocks at our door." In the wake of advances in unsupervised, self-improving machine learning, a small but influential community of thinkers is considering Wiener's words again. In Possible Minds, John Brockman gathers their disparate visions of where AI might be taking us. The fruit of the long history of Brockman's profound engagement with the most important scientific minds who have been thinking about AI--from Alison Gopnik and David Deutsch to Frank Wilczek and Stephen Wolfram--Possible Minds is an ideal introduction to the landscape of crucial issues AI presents. The collision between opposing perspectives is salutary and exhilarating; some of these figures, such as computer scientist Stuart Russell, Skype co-founder Jaan Tallinn, and physicist Max Tegmark, are deeply concerned with the threat of AI, including the existential one, while others, notably robotics entrepreneur Rodney Brooks, philosopher Daniel Dennett, and bestselling author Steven Pinker, have a very different view. Serious, searching and authoritative, Possible Minds lays out the intellectual landscape of one of the most important topics of our time.

Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Download or read book Intelligent Tutoring Systems written by Stefan Trausan-Matu and published by Springer. This book was released on 2014-05-24 with total page 727 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th International Conference on Intelligent Tutoring Systems, ITS 2014, held in Honolulu, HI, USA, in June 2014. The 31 revised full papers, 45 short papers and 27 posters presented were carefully viewed and selected from 177 submissions. The specific theme of the ITS 2014 conference is "Creating fertile soil for learning interactions". Besides that, the highly interdisciplinary ITS conferences bring together researchers in computer science, learning sciences, cognitive and educational psychology, sociology, cognitive science, artificial intelligence, machine learning and linguistics. The papers are organized in topical sections on affect; multimodality and metacognition; collaborative learning; data mining and student behavior; dialogue and discourse; generating hints, scaffolds and questions; game-based learning and simulation; graphical representations and learning; student strategies and problem solving; scaling ITS and assessment.

Download or read book Elements of Causal Inference written by Jonas Peters and published by MIT Press. This book was released on 2017-12-29 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

Download or read book Artificial Intelligence in Education written by Cristina Conati and published by Springer. This book was released on 2015-06-16 with total page 955 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 17th International Conference on Artificial Intelligence in Education, AIED 2015, held in Madrid, Spain, in June 2015. The 50 revised full papers presented together with 3 keynotes, 79 poster presentations, 13 doctoral consortium papers, 16 workshop abstracts, and 8 interactive event papers were carefully reviewed and selected from numerous submissions. The conference provides opportunities for the cross-fertilization of approaches, techniques and ideas from the many fields that comprise AIED, including computer science, cognitive and learning sciences, education, game design, psychology, sociology, linguistics, as well as many domain-specific areas.

Download or read book The Exposome written by Gary W. Miller and published by Academic Press. This book was released on 2020-06-02 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Exposome: A New Paradigm for the Environment and Health, Second Edition, is a thoroughly expanded and updated edition of The Exposome: A Primer, the first book dedicated to the topic. This new release outlines the purpose and scope of this emerging field of study, its practical applications, and how it complements a broad range of disciplines. The book contains sections on -omics-based technologies, newer detection methods, managing and integrating exposome data (including maps, models, computation and systems biology), and more. Both students and scientists in toxicology, environmental health, epidemiology and public health will benefit from this rigorous, yet readable, overview. This updated edition includes a more in-depth examination of the exposome, including full references, further reading and thought questions. - Addresses an emerging field that connects with other exciting disciplines - Written by a single author who is a leader in the field - Includes new content that widely expands on the first edition

Download or read book Routledge Handbook of Macroeconomic Methodology written by Jesper Jespersen and published by Taylor & Francis. This book was released on 2023-06-20 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present macroeconomic crisis has demonstrated that a deeper understanding of the importance of relevant macroeconomic theories and methods is wanting. Additionally, lack of methodological awareness is behind much of the disagreement within macroeconomics which, looked upon from outside, often appears incomprehensible. The Handbook gives a structured presentation of the study of principles and procedures by which macroeconomics is researched, taught and communicated both within academia and to a wider audience, and why specific theories, research strategies and teaching are preferred. The principles of selecting theory relevant to real-world problems are the core of methodology. This book contains a broad range of arguments behind theory construction and appraisal and the consequences of these choices within the field of macroeconomics. An international range of experts provide clear analysis of key concepts, ideas and principles to give academics, students and others a better understanding of the macroeconomics behind policy conclusions which are put forward at different levels.