Bayesian Two Stage Robust Causal Modeling With Instrumental Variables Using Students T Distributions

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Bayesian Inference

Author : Javier Prieto Tejedor
Publisher : BoD – Books on Demand
ISBN 13 : 9535135775
Total Pages : 379 pages
Book Rating : 4.5/5 (351 download)

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Book Synopsis Bayesian Inference by : Javier Prieto Tejedor

Download or read book Bayesian Inference written by Javier Prieto Tejedor and published by BoD – Books on Demand. This book was released on 2017-11-02 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: The range of Bayesian inference algorithms and their different applications has been greatly expanded since the first implementation of a Kalman filter by Stanley F. Schmidt for the Apollo program. Extended Kalman filters or particle filters are just some examples of these algorithms that have been extensively applied to logistics, medical services, search and rescue operations, or automotive safety, among others. This book takes a look at both theoretical foundations of Bayesian inference and practical implementations in different fields. It is intended as an introductory guide for the application of Bayesian inference in the fields of life sciences, engineering, and economics, as well as a source document of fundamentals for intermediate Bayesian readers.

Quantitative Psychology

Author : L. Andries van der Ark
Publisher : Springer
ISBN 13 : 3319562940
Total Pages : 419 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Quantitative Psychology by : L. Andries van der Ark

Download or read book Quantitative Psychology written by L. Andries van der Ark and published by Springer. This book was released on 2017-06-06 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceedings volume compiles and expands on selected and peer reviewed presentations given at the 81st Annual Meeting of the Psychometric Society (IMPS), organized by the University of North Carolina at Greensboro, and held in Asheville, North Carolina, July 11th to 17th, 2016. IMPS is one of the largest international meetings focusing on quantitative measurement in psychology, education, and the social sciences, both in terms of participants and number of presentations. The meeting built on the Psychometric Society's mission to share quantitative methods relevant to psychology, addressing a diverse set of psychometric topics including item response theory, factor analysis, structural equation modeling, time series analysis, mediation analysis, cognitive diagnostic models, and multi-level models. Selected presenters were invited to revise and expand their contributions and to have them peer reviewed and published in this proceedings volume. Previous volumes to showcase work from the Psychometric Society’s meetings are New Developments in Quantitative Psychology: Presentations from the 77th Annual Psychometric Society Meeting (Springer, 2013), Quantitative Psychology Research: The 78th Annual Meeting of the Psychometric Society (Springer, 2015), Quantitative Psychology Research: The 79th Annual Meeting of the Psychometric Society, Madison, Wisconsin, 2014 (Springer, 2015), and Quantitative Psychology Research: The 80th Annual Meeting of the Psychometric Society, Beijing, 2015 (Springer, 2016).

An Introduction to Causal Inference

Author : Judea Pearl
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781507894293
Total Pages : 0 pages
Book Rating : 4.8/5 (942 download)

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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.

The Book of Why

Author : Judea Pearl
Publisher : Basic Books
ISBN 13 : 0465097618
Total Pages : 432 pages
Book Rating : 4.4/5 (65 download)

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Book Synopsis The Book of Why by : Judea Pearl

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.

Bayesian Data Analysis, Third Edition

Author : Andrew Gelman
Publisher : CRC Press
ISBN 13 : 1439840954
Total Pages : 677 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman

Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

Data Analysis Using Regression and Multilevel/Hierarchical Models

Author : Andrew Gelman
Publisher : Cambridge University Press
ISBN 13 : 9780521686891
Total Pages : 654 pages
Book Rating : 4.6/5 (868 download)

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Book Synopsis Data Analysis Using Regression and Multilevel/Hierarchical Models by : Andrew Gelman

Download or read book Data Analysis Using Regression and Multilevel/Hierarchical Models written by Andrew Gelman and published by Cambridge University Press. This book was released on 2007 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

Causal Inference in Statistics

Author : Judea Pearl
Publisher : John Wiley & Sons
ISBN 13 : 1119186862
Total Pages : 162 pages
Book Rating : 4.1/5 (191 download)

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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.

Methods Matter

Author : Richard J. Murnane
Publisher : Oxford University Press
ISBN 13 : 0199890153
Total Pages : 414 pages
Book Rating : 4.1/5 (998 download)

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Book Synopsis Methods Matter by : Richard J. Murnane

Download or read book Methods Matter written by Richard J. Murnane and published by Oxford University Press. This book was released on 2010-09-17 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Educational policy-makers around the world constantly make decisions about how to use scarce resources to improve the education of children. Unfortunately, their decisions are rarely informed by evidence on the consequences of these initiatives in other settings. Nor are decisions typically accompanied by well-formulated plans to evaluate their causal impacts. As a result, knowledge about what works in different situations has been very slow to accumulate. Over the last several decades, advances in research methodology, administrative record keeping, and statistical software have dramatically increased the potential for researchers to conduct compelling evaluations of the causal impacts of educational interventions, and the number of well-designed studies is growing. Written in clear, concise prose, Methods Matter: Improving Causal Inference in Educational and Social Science Research offers essential guidance for those who evaluate educational policies. Using numerous examples of high-quality studies that have evaluated the causal impacts of important educational interventions, the authors go beyond the simple presentation of new analytical methods to discuss the controversies surrounding each study, and provide heuristic explanations that are also broadly accessible. Murnane and Willett offer strong methodological insights on causal inference, while also examining the consequences of a wide variety of educational policies implemented in the U.S. and abroad. Representing a unique contribution to the literature surrounding educational research, this landmark text will be invaluable for students and researchers in education and public policy, as well as those interested in social science.

Introduction to Probability

Author : Joseph K. Blitzstein
Publisher : CRC Press
ISBN 13 : 1466575573
Total Pages : 599 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Introduction to Probability by : Joseph K. Blitzstein

Download or read book Introduction to Probability written by Joseph K. Blitzstein and published by CRC Press. This book was released on 2014-07-24 with total page 599 pages. Available in PDF, EPUB and Kindle. Book excerpt: Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.

Discrete Choice Methods with Simulation

Author : Kenneth Train
Publisher : Cambridge University Press
ISBN 13 : 0521766559
Total Pages : 399 pages
Book Rating : 4.5/5 (217 download)

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Book Synopsis Discrete Choice Methods with Simulation by : Kenneth Train

Download or read book Discrete Choice Methods with Simulation written by Kenneth Train and published by Cambridge University Press. This book was released on 2009-07-06 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.

The Deep Learning Workshop

Author : Mirza Rahim Baig
Publisher : Packt Publishing Ltd
ISBN 13 : 1839210567
Total Pages : 473 pages
Book Rating : 4.8/5 (392 download)

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Book Synopsis The Deep Learning Workshop by : Mirza Rahim Baig

Download or read book The Deep Learning Workshop written by Mirza Rahim Baig and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret text Key Features Understand how to implement deep learning with TensorFlow and Keras Learn the fundamentals of computer vision and image recognition Study the architecture of different neural networks Book Description Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout. The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You'll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you'll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you'll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis. By the end of this deep learning book, you'll have learned the skills essential for building deep learning models with TensorFlow and Keras. What you will learn Understand how deep learning, machine learning, and artificial intelligence are different Develop multilayer deep neural networks with TensorFlow Implement deep neural networks for multiclass classification using Keras Train CNN models for image recognition Handle sequence data and use it in conjunction with RNNs Build a GAN to generate high-quality synthesized images Who this book is for If you are interested in machine learning and want to create and train deep learning models using TensorFlow and Keras, this workshop is for you. A solid understanding of Python and its packages, along with basic machine learning concepts, will help you to learn the topics quickly.

Statistical Rethinking

Author : Richard McElreath
Publisher : CRC Press
ISBN 13 : 1315362619
Total Pages : 488 pages
Book Rating : 4.3/5 (153 download)

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Book Synopsis Statistical Rethinking by : Richard McElreath

Download or read book Statistical Rethinking written by Richard McElreath and published by CRC Press. This book was released on 2018-01-03 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

Economic Fables

Author : Ariel Rubinstein
Publisher : Open Book Publishers
ISBN 13 : 1906924775
Total Pages : 266 pages
Book Rating : 4.9/5 (69 download)

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Book Synopsis Economic Fables by : Ariel Rubinstein

Download or read book Economic Fables written by Ariel Rubinstein and published by Open Book Publishers. This book was released on 2012 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: "I had the good fortune to grow up in a wonderful area of Jerusalem, surrounded by a diverse range of people: Rabbi Meizel, the communist Sala Marcel, my widowed Aunt Hannah, and the intellectual Yaacovson. As far as I'm concerned, the opinion of such people is just as authoritative for making social and economic decisions as the opinion of an expert using a model." Part memoir, part crash-course in economic theory, this deeply engaging book by one of the world's foremost economists looks at economic ideas through a personal lens. Together with an introduction to some of the central concepts in modern economic thought, Ariel Rubinstein offers some powerful and entertaining reflections on his childhood, family and career. In doing so, he challenges many of the central tenets of game theory, and sheds light on the role economics can play in society at large. Economic Fables is as thought-provoking for seasoned economists as it is enlightening for newcomers to the field.

The SAGE Handbook of Regression Analysis and Causal Inference

Author : Henning Best
Publisher : SAGE
ISBN 13 : 1473908353
Total Pages : 425 pages
Book Rating : 4.4/5 (739 download)

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Book Synopsis The SAGE Handbook of Regression Analysis and Causal Inference by : Henning Best

Download or read book The SAGE Handbook of Regression Analysis and Causal Inference written by Henning Best and published by SAGE. This book was released on 2013-12-20 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: ′The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.′ - John Fox, Professor, Department of Sociology, McMaster University ′The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.′ - Ben Jann, Executive Director, Institute of Sociology, University of Bern ′Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.′ -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis.

Targeted Learning

Author : Mark J. van der Laan
Publisher : Springer Science & Business Media
ISBN 13 : 1441997822
Total Pages : 628 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis Targeted Learning by : Mark J. van der Laan

Download or read book Targeted Learning written by Mark J. van der Laan and published by Springer Science & Business Media. This book was released on 2011-06-17 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt: The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.

Elements of Causal Inference

Author : Jonas Peters
Publisher : MIT Press
ISBN 13 : 0262037319
Total Pages : 289 pages
Book Rating : 4.2/5 (62 download)

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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.

Causation, Prediction, and Search

Author : Peter Spirtes
Publisher : Springer Science & Business Media
ISBN 13 : 1461227488
Total Pages : 551 pages
Book Rating : 4.4/5 (612 download)

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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.