Download or read book Inference and Intervention written by Michael D. Ryall and published by Routledge. This book was released on 2013-08-22 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ryall and Bramson's Inference and Intervention is the first textbook on causal modeling with Bayesian networks for business applications. In a world of resource scarcity, a decision about which business elements to control or change – as the authors put it, a managerial intervention – must precede any decision on how to control or change them, and understanding causality is crucial to making effective interventions. The authors cover the full spectrum of causal modeling techniques useful for the managerial role, whether for intervention, situational assessment, strategic decision-making, or forecasting. From the basic concepts and nomenclature of causal modeling to decision tree analysis, qualitative methods, and quantitative modeling tools, this book offers a toolbox for MBA students and business professionals to make successful decisions in a managerial setting.
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 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 Inference and Intervention written by Michael D. Ryall and published by Routledge. This book was released on 2013-08-22 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ryall and Bramson's Inference and Intervention is the first textbook on causal modeling with Bayesian networks for business applications. In a world of resource scarcity, a decision about which business elements to control or change – as the authors put it, a managerial intervention – must precede any decision on how to control or change them, and understanding causality is crucial to making effective interventions. The authors cover the full spectrum of causal modeling techniques useful for the managerial role, whether for intervention, situational assessment, strategic decision-making, or forecasting. From the basic concepts and nomenclature of causal modeling to decision tree analysis, qualitative methods, and quantitative modeling tools, this book offers a toolbox for MBA students and business professionals to make successful decisions in a managerial setting.
Download or read book Causality written by Judea Pearl and published by Cambridge University Press. This book was released on 2009-09-14 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence ...
Download or read book Causal Inference written by Scott Cunningham and published by Yale University Press. This book was released on 2021-01-26 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible, contemporary introduction to the methods for determining cause and effect in the Social Sciences “Causation versus correlation has been the basis of arguments—economic and otherwise—since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It’s rare that a book prompts readers to expand their outlook; this one did for me.”—Marvin Young (Young MC) Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages.
Download or read book Inferences during Reading written by Edward J. O'Brien and published by Cambridge University Press. This book was released on 2015-04-16 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: Inferencing is defined as 'the act of deriving logical conclusions from premises known or assumed to be true', and it is one of the most important processes necessary for successful comprehension during reading. This volume features contributions by distinguished researchers in cognitive psychology, educational psychology, and neuroscience on topics central to our understanding of the inferential process during reading. The chapters cover aspects of inferencing that range from the fundamental bottom-up processes that form the basis for an inference to occur, to the more strategic processes that transpire when a reader is engaged in literary understanding of a text. Basic activation mechanisms, word-level inferencing, methodological considerations, inference validation, causal inferencing, emotion, development of inferences processes as a skill, embodiment, contributions from neuroscience, and applications to naturalistic text are all covered as well as expository text, online learning materials, and literary immersion.
Download or read book Artificial Intelligence and Causal Inference written by Momiao Xiong and published by CRC Press. This book was released on 2022-02-03 with total page 666 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. Understanding, transfer and generalization are major principles that give rise intelligence. One of a key component for understanding is causal inference. Causal inference includes intervention, domain shift learning, temporal structure and counterfactual thinking as major concepts to understand causation and reasoning. Unfortunately, these essential components of the causality are often overlooked by machine learning, which leads to some failure of the deep learning. AI and causal inference involve (1) using AI techniques as major tools for causal analysis and (2) applying the causal concepts and causal analysis methods to solving AI problems. The purpose of this book is to fill the gap between the AI and modern causal analysis for further facilitating the AI revolution. This book is ideal for graduate students and researchers in AI, data science, causal inference, statistics, genomics, bioinformatics and precision medicine. Key Features: Cover three types of neural networks, formulate deep learning as an optimal control problem and use Pontryagin’s Maximum Principle for network training. Deep learning for nonlinear mediation and instrumental variable causal analysis. Construction of causal networks is formulated as a continuous optimization problem. Transformer and attention are used to encode-decode graphics. RL is used to infer large causal networks. Use VAE, GAN, neural differential equations, recurrent neural network (RNN) and RL to estimate counterfactual outcomes. AI-based methods for estimation of individualized treatment effect in the presence of network interference.
Download or read book PRICAI 2004 Trends in Artificial Intelligence written by Chengqi Zhang and published by Springer. This book was released on 2004-09-21 with total page 1043 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Pacific Rim International Conference on Artificial Intelligence (PRICAI) is a biennial international event which focuses on Artificial Intelligence (AI) theories and technologies, and their applications which are of social and economic importance for countries in the Pacific Rim region. Seven earlier conferences were held in: Nagoya, Japan (1990); Seoul, Korea (1992); Beijing, China (1994); Cairns, Australia (1996); Singapore (1998); Melbourne, Australia (2000); and Tokyo, Japan (2002). PRICAI 2004 was the eigth in the series and was held in Auckland, New Zealand in August 2004. PRICAI 2004 had attracted a historical record number of submissions, a total of 356 papers. After careful reviews by at least two international Program Committee members or referees, 94 papers were accepted as full papers (27%) and 54 papers (15%) were accepted as posters. Authors of accepted papers came from 27 countries. This volume of the proceedings contains all the 94 full papers but only a 2-page - tended abstract of each of the accepted posters. The full papers were categorized into four sections, namely: AI foundations, computational intelligence, AI technologies and systems, and AI specific application areas. Among the papers submitted, we found “Agent Technology” to be the area having the most papers submitted. This was followed by “Evolutionary Computing”, “Computational Learning”, and “Image Processing”.
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 Inferring Grades 1 2 written by Frank Schaffer Publications and published by Carson-Dellosa Publishing. This book was released on 2012-01-03 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: Inferring is an essential reading comprehension skill for all subject areas. Help students understand inferring using Spotlight on Reading: Inferring for grades 1Ð2. This 48-page book includes a variety of high-interest lessons and activities that make learning fun! The exercises increase in difficulty as the book progresses, so students practice more-advanced skills as they work. With a variety of formats, teachers can provide direct instruction, reinforcement, and independent practice throughout the year. This book is perfect for practice at home and school and includes an answer key. Aligns with Common Core State Standards and Canadian provincial standards.
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 Inference and Prediction in Large Dimensions written by Denis Bosq and published by John Wiley & Sons. This book was released on 2008-03-11 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a predominantly theoretical coverage of statistical prediction, with some potential applications discussed, when data and/ or parameters belong to a large or infinite dimensional space. It develops the theory of statistical prediction, non-parametric estimation by adaptive projection – with applications to tests of fit and prediction, and theory of linear processes in function spaces with applications to prediction of continuous time processes. This work is in the Wiley-Dunod Series co-published between Dunod (www.dunod.com) and John Wiley and Sons, Ltd.
Download or read book Inference and Intervention written by Michael D. Ryall and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ryall and Bramson's Inference and Intervention is the first textbook on causal modeling with Bayesian networks for business applications. In a world of resource scarcity, a decision about which business elements to control or change - as the authors put it, a managerial intervention - must precede any decision on how to control or change them, and understanding causality is crucial to making effective interventions. The authors cover the full spectrum of causal modeling techniques useful for the managerial role, whether for intervention, situational assessment, strategic decision-making, or forecasting. From the basic concepts and nomenclature of causal modeling to decision tree analysis, qualitative methods, and quantitative modeling tools, this book offers a toolbox for MBA students and business professionals to make successful decisions in a managerial setting.
Download or read book Emerging Issues in Causal Inference for Intervention Trials written by Qi Long and published by . This book was released on 2005 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Design Inference written by William A. Dembski and published by Cambridge University Press. This book was released on 1998-09-13 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: The design inference uncovers intelligent causes by isolating their key trademark: specified events of small probability. Just about anything that happens is highly improbable, but when a highly improbable event is also specified (i.e. conforms to an independently given pattern) undirected natural causes lose their explanatory power. Design inferences can be found in a range of scientific pursuits from forensic science to research into the origins of life to the search for extraterrestrial intelligence. This challenging and provocative 1998 book shows how incomplete undirected causes are for science and breathes new life into classical design arguments. It will be read with particular interest by philosophers of science and religion, other philosophers concerned with epistemology and logic, probability and complexity theorists, and statisticians.
Download or read book Explanation in Causal Inference written by Tyler J. VanderWeele and published by Oxford University Press, USA. This book was released on 2015 with total page 729 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book begins with a comprehensive introduction to mediation analysis, including chapters on concepts for mediation, regression-based methods, sensitivity analysis, time-to-event outcomes, methods for multiple mediators, methods for time-varying mediation and longitudinal data, and relations between mediation and other concepts involving intermediates such as surrogates, principal stratification, instrumental variables, and Mendelian randomization. The second part of the book concerns interaction or "moderation," including concepts for interaction, statistical interaction, confounding and interaction, mechanistic interaction, bias analysis for interaction, interaction in genetic studies, and power and sample-size calculation for interaction. The final part of the book provides comprehensive discussion about the relationships between mediation and interaction and unites these concepts within a single framework.
