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Causation Prediction And Search
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Book Synopsis Causation, Prediction, and Search by : Peter Spirtes
Download or read book Causation, Prediction, and Search written by Peter Spirtes and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended for anyone, regardless of discipline, who is interested in the use of statistical methods to help obtain scientific explanations or to predict the outcomes of actions, experiments or policies. Much of G. Udny Yule's work illustrates a vision of statistics whose goal is to investigate when and how causal influences may be reliably inferred, and their comparative strengths estimated, from statistical samples. Yule's enterprise has been largely replaced by Ronald Fisher's conception, in which there is a fundamental cleavage between experimental and non experimental inquiry, and statistics is largely unable to aid in causal inference without randomized experimental trials. Every now and then members of the statistical community express misgivings about this turn of events, and, in our view, rightly so. Our work represents a return to something like Yule's conception of the enterprise of theoretical statistics and its potential practical benefits. If intellectual history in the 20th century had gone otherwise, there might have been a discipline to which our work belongs. As it happens, there is not. We develop material that belongs to statistics, to computer science, and to philosophy; the combination may not be entirely satisfactory for specialists in any of these subjects. We hope it is nonetheless satisfactory for its purpose.
Book Synopsis Computation, Causation, and Discovery by : Clark N. Glymour
Download or read book Computation, Causation, and Discovery written by Clark N. Glymour and published by . This book was released on 1999 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Discovering Causal Structure by : Clark Glymour
Download or read book Discovering Causal Structure written by Clark Glymour and published by Academic Press. This book was released on 2014-05-10 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling provides information pertinent to the fundamental aspects of a computer program called TETRAD. This book discusses the version of the TETRAD program, which is designed to assist in the search for causal explanations of statistical data. or alternative models. This text then examines the notion of applying artificial intelligence methods to problems of statistical model specification. Other chapters consider how the TETRAD program can help to find god alternative models where they exist, and how it can help detect the existence of important neglected variables. This book discusses as well the procedures for specifying a model or models to account for non-experimental or quasi-experimental data. The final chapter presents a description of the format of input files and a description of each command. This book is a valuable resource for social scientists and researchers.
Book Synopsis Elements of Causal Inference by : Jonas Peters
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.
Author :Judea Pearl Publisher :Createspace Independent Publishing Platform ISBN 13 :9781507894293 Total Pages :0 pages Book Rating :4.8/5 (942 download)
Book Synopsis An Introduction to Causal Inference by : Judea Pearl
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 Causal Learning written by and published by Academic Press. This book was released on 1996-09-26 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Psychology of Learning and Motivation publishes empirical and theoretical contributions in cognitive and experimental psychology, ranging from classical and instrumental conditions to complex learning and problem solving. This guest-edited special volume is devoted to current research and discussion on associative versus cognitive accounts of learning. Written by major investigators in the field, topics include all aspects of causal learning in an open forum in which different approaches are brought together. - Up-to-date review of the literature - Discusses recent controversies - Presents major advances in understanding causal learning - Synthesizes contrasting approaches - Includes important empirical contributions - Written by leading researchers in the field
Book Synopsis Practical Approaches to Causal Relationship Exploration by : Jiuyong Li
Download or read book Practical Approaches to Causal Relationship Exploration written by Jiuyong Li and published by Springer. This book was released on 2015-03-02 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt: This brief presents four practical methods to effectively explore causal relationships, which are often used for explanation, prediction and decision making in medicine, epidemiology, biology, economics, physics and social sciences. The first two methods apply conditional independence tests for causal discovery. The last two methods employ association rule mining for efficient causal hypothesis generation, and a partial association test and retrospective cohort study for validating the hypotheses. All four methods are innovative and effective in identifying potential causal relationships around a given target, and each has its own strength and weakness. For each method, a software tool is provided along with examples demonstrating its use. Practical Approaches to Causal Relationship Exploration is designed for researchers and practitioners working in the areas of artificial intelligence, machine learning, data mining, and biomedical research. The material also benefits advanced students interested in causal relationship discovery.
Book Synopsis Causal Inference in Statistics by : Judea Pearl
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.
Book Synopsis Causal Inference by : Miquel A. Hernan
Download or read book Causal Inference written by Miquel A. Hernan and published by CRC Press. This book was released on 2019-07-07 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of causal inference methods is growing exponentially in fields that deal with observational data. Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. With a wide range of detailed, worked examples using real epidemiologic data as well as software for replicating the analyses, the text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data.
Book Synopsis Making Things Happen by : James Woodward
Download or read book Making Things Happen written by James Woodward and published by Oxford University Press. This book was released on 2005-10-27 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Making Things Happen, James Woodward develops a new and ambitious comprehensive theory of causation and explanation that draws on literature from a variety of disciplines and which applies to a wide variety of claims in science and everyday life. His theory is a manipulationist account, proposing that causal and explanatory relationships are relationships that are potentially exploitable for purposes of manipulation and control. This account has its roots in the commonsense idea that causes are means for bringing about effects; but it also draws on a long tradition of work in experimental design, econometrics, and statistics. Woodward shows how these ideas may be generalized to other areas of science from the social scientific and biomedical contexts for which they were originally designed. He also provides philosophical foundations for the manipulationist approach, drawing out its implications, comparing it with alternative approaches, and defending it from common criticisms. In doing so, he shows how the manipulationist account both illuminates important features of successful causal explanation in the natural and social sciences, and avoids the counterexamples and difficulties that infect alternative approaches, from the deductive-nomological model onwards. Making Things Happen will interest philosophers working in the philosophy of science, the philosophy of social science, and metaphysics, and as well as anyone interested in causation, explanation, and scientific methodology.
Book Synopsis Causality, Probability, and Time by : Samantha Kleinberg
Download or read book Causality, Probability, and Time written by Samantha Kleinberg and published by Cambridge University Press. This book was released on 2013 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a new approach to causal inference and explanation, addressing both the timing and complexity of relationships.
Book Synopsis Mechanism and Causality in Biology and Economics by : Hsiang-Ke Chao
Download or read book Mechanism and Causality in Biology and Economics written by Hsiang-Ke Chao and published by Springer Science & Business Media. This book was released on 2013-07-31 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume addresses fundamental issues in the philosophy of science in the context of two most intriguing fields: biology and economics. Written by authorities and experts in the philosophy of biology and economics, Mechanism and Causality in Biology and Economics provides a structured study of the concepts of mechanism and causality in these disciplines and draws careful juxtapositions between philosophical apparatus and scientific practice. By exploring the issues that are most salient to the contemporary philosophies of biology and economics and by presenting comparative analyses, the book serves as a platform not only for gaining mutual understanding between scientists and philosophers of the life sciences and those of the social sciences, but also for sharing interdisciplinary research that combines both philosophical concepts in both fields. The book begins by defining the concepts of mechanism and causality in biology and economics, respectively. The second and third parts investigate philosophical perspectives of various causal and mechanistic issues in scientific practice in the two fields. These two sections include chapters on causal issues in the theory of evolution; experiments and scientific discovery; representation of causal relations and mechanism by models in economics. The concluding section presents interdisciplinary studies of various topics concerning extrapolation of life sciences and social sciences, including chapters on the philosophical investigation of conjoining biological and economic analyses with, respectively, demography, medicine and sociology.
Book Synopsis Actual Causality by : Joseph Y. Halpern
Download or read book Actual Causality written by Joseph Y. Halpern and published by MIT Press. This book was released on 2016-08-12 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explores actual causality, and such related notions as degree of responsibility, degree of blame, and causal explanation. The goal is to arrive at a definition of causality that matches our natural language usage and is helpful, for example, to a jury deciding a legal case, a programmer looking for the line of code that cause some software to fail, or an economist trying to determine whether austerity caused a subsequent depression.
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 ...
Book Synopsis Mathematical Models for Handling Partial Knowledge in Artificial Intelligence by : Giulianella Coletti
Download or read book Mathematical Models for Handling Partial Knowledge in Artificial Intelligence written by Giulianella Coletti and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge acquisition is one of the most important aspects influencing the quality of methods used in artificial intelligence and the reliability of expert systems. The various issues dealt with in this volume concern many different approaches to the handling of partial knowledge and to the ensuing methods for reasoning and decision making under uncertainty, as applied to problems in artificial intelligence. The volume is composed of the invited and contributed papers presented at the Workshop on Mathematical Models for Handling Partial Knowledge in Artificial Intelligence, held at the Ettore Majorana Center for Scientific Culture of Erice (Sicily, Italy) on June 19-25, 1994, in the framework of the International School of Mathematics "G.Stampacchia". It includes also a transcription of the roundtable held during the workshop to promote discussions on fundamental issues, since in the choice of invited speakers we have tried to maintain a balance between the various schools of knowl edge and uncertainty modeling. Choquet expected utility models are discussed in the paper by Alain Chateauneuf: they allow the separation of perception of uncertainty or risk from the valuation of outcomes, and can be of help in decision mak ing. Petr Hajek shows that reasoning in fuzzy logic may be put on a strict logical (formal) basis, so contributing to our understanding of what fuzzy logic is and what one is doing when applying fuzzy reasoning.
Book Synopsis Forecasting: principles and practice by : Rob J Hyndman
Download or read book Forecasting: principles and practice written by Rob J Hyndman and published by OTexts. This book was released on 2018-05-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Book Synopsis Interpretable Machine Learning by : Christoph Molnar
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.
