*A Primer*

Author: Judea Pearl,Madelyn Glymour,Nicholas P. Jewell

Publisher: John Wiley & Sons

ISBN: 1119186854

Category: Mathematics

Page: 160

View: 7643

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Search Results for: causal-inference-in-statistics-a-primer

## Causal Inference in Statistics

Many of the concepts and terminology surrounding modern causal inference can be quite intimidating to the novice. Judea Pearl presents a book ideal for beginners in statistics, providing a comprehensive introduction to the field of causality. Examples from classical statistics are presented throughout to demonstrate the need for causality in resolving decision-making dilemmas posed by data. Causal methods are also compared to traditional statistical methods, whilst questions are provided at the end of each section to aid student learning.
## Causality

Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence (AI) and systems from The Cognitive Science Society.
## Elements of Causal Inference

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.
## An Introduction to Causal Inference

This book 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 underlie 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), 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: those about (1) the effects of potential interventions, (2) probabilities of counterfactuals, and (3) 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.
## The Book of Why

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.
## Causality, Probability, and Time

"This book presents a new approach to causal inference and explanation, addressing both the timing and complexity of relationships. The method's feasibility and success is demonstrated through theoretical and experimental case studies"--
## Observation and Experiment

In the face of conflicting claims about some treatments, behaviors, and policies, the question arises: What is the most scientifically rigorous way to draw conclusions about cause and effect in the study of humans? In this introduction to causal inference, Paul Rosenbaum explains key concepts and methods through real-world examples.
## Causal inference

## Counterfactuals and Causal Inference

This new edition aims to convince social scientists to take a counterfactual approach to the core questions of their fields.
## Causal Inference for Statistics, Social, and Biomedical Sciences

Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.
## Culture and Health

Culture and Health offers an overview of different areas of culture and health, building on foundations of medical anthropology and health behavior theory. It shows how to address the challenges of cross-cultural medicine through interdisciplinary cultural-ecological models and personal and institutional developmental approaches to cross-cultural adaptation and competency. The book addresses the perspectives of clinically applied anthropology, trans-cultural psychiatry and the medical ecology, critical medical anthropology and symbolic paradigms as frameworks for enhanced comprehension of health and the medical encounter. Includes cultural case studies, applied vignettes, and self-assessments.
## Explanation in Causal Inference

"A comprehensive book on methods for mediation and interaction. The only book to approach this topic from the perspective of causal inference. Numerous software tools provided. Easy-to-read and accessible. Examples drawn from diverse fields. An essential reference for anyone conducting empirical research in the biomedical or social sciences"--
## Probabilistic Reasoning in Intelligent Systems

Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
## Why

Can drinking coffee help people live longer? What makes a stock’s price go up? Why did you get the flu? Causal questions like these arise on a regular basis, but most people likely have not thought deeply about how to answer them. This book helps you think about causality in a structured way: What is a cause, what are causes good for, and what is compelling evidence of causality? Author Samantha Kleinberg shows you how to develop a set of tools for thinking more critically about causes. You’ll learn how to question claims, identify causes, make decisions based on causal information, and verify causes through further tests. Whether it’s figuring out what data you need, or understanding that the way you collect and prepare data affects the conclusions you can draw from it, Why will help you sharpen your causal inference skills.
## Philosophy of Statistics

Statisticians and philosophers of science have many common interests but restricted communication with each other. This volume aims to remedy these shortcomings. It provides state-of-the-art research in the area of philosophy of statistics by encouraging numerous experts to communicate with one another without feeling “restricted by their disciplines or thinking “piecemeal in their treatment of issues. A second goal of this book is to present work in the field without bias toward any particular statistical paradigm. Broadly speaking, the essays in this Handbook are concerned with problems of induction, statistics and probability. For centuries, foundational problems like induction have been among philosophers’ favorite topics; recently, however, non-philosophers have increasingly taken a keen interest in these issues. This volume accordingly contains papers by both philosophers and non-philosophers, including scholars from nine academic disciplines. Provides a bridge between philosophy and current scientific findings Covers theory and applications Encourages multi-disciplinary dialogue
## Actual Causality

Causality plays a central role in the way people structure the world; we constantly seek causal explanations for our observations. But what does it even mean that an event C "actually caused" event E? The problem of defining actual causation goes beyond mere philosophical speculation. For example, in many legal arguments, it is precisely what needs to be established in order to determine responsibility. The philosophy literature has been struggling with the problem of defining causality since Hume.In this book, Joseph Halpern 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.Halpern applies and expands an approach to causality that he and Judea Pearl developed, based on structural equations. He carefully formulates a definition of causality, and building on this, defines degree of responsibility, degree of blame, and causal explanation. He concludes by discussing how these ideas can be applied to such practical problems as accountability and program verification. Technical details are generally confined to the final section of each chapter and can be skipped by non-mathematical readers.
## I Am Jewish

Inspired by the final words of murdered journalist Daniel Pearl, a collection of personal essays, reflections, theological statements, reminiscences, and stories expresses what being Jewish means to such contributors as Alan Dershowitz, Kirk Douglas, Theodore Bikel, Dianne Feinstein, Daniel Schorr, Larry King, Harold Kushner, Norman Lear, Joe Lieberman, and many others.
## Statistical Inference as Severe Testing

Unlock today's statistical controversies and irreproducible results by viewing statistics as probing and controlling errors.
## Causality

A state of the art volume on statistical causality Causality: Statistical Perspectives and Applications presents a wide-ranging collection of seminal contributions by renowned experts in the field, providing a thorough treatment of all aspects of statistical causality. It covers the various formalisms in current use, methods for applying them to specific problems, and the special requirements of a range of examples from medicine, biology and economics to political science. This book: Provides a clear account and comparison of formal languages, concepts and models for statistical causality. Addresses examples from medicine, biology, economics and political science to aid the reader's understanding. Is authored by leading experts in their field. Is written in an accessible style. Postgraduates, professional statisticians and researchers in academia and industry will benefit from this book.
## Causality in a Social World

Causality in a Social World introduces innovative new statistical research and strategies for investigating moderated intervention effects, mediated intervention effects, and spill-over effects using experimental or quasi-experimental data. The book uses potential outcomes to define causal effects, explains and evaluates identification assumptions using application examples, and compares innovative statistical strategies with conventional analysis methods. Whilst highlighting the crucial role of good research design and the evaluation of assumptions required for identifying causal effects in the context of each application, the author demonstrates that improved statistical procedures will greatly enhance the empirical study of causal relationship theory. Applications focus on interventions designed to improve outcomes for participants who are embedded in social settings, including families, classrooms, schools, neighbourhoods, and workplaces.

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*A Primer*

Author: Judea Pearl,Madelyn Glymour,Nicholas P. Jewell

Publisher: John Wiley & Sons

ISBN: 1119186854

Category: Mathematics

Page: 160

View: 7643

Author: Judea Pearl

Publisher: Cambridge University Press

ISBN: 1139643983

Category: Science

Page: N.A

View: 6110

*Foundations and Learning Algorithms*

Author: Jonas Peters,Dominik Janzing,Bernhard Schölkopf

Publisher: MIT Press

ISBN: 0262037319

Category: Computers

Page: 288

View: 2307

Author: Judea Pearl

Publisher: CreateSpace

ISBN: 9781507894293

Category:

Page: 94

View: 730

*The New Science of Cause and Effect*

Author: Judea Pearl,Dana Mackenzie

Publisher: Basic Books

ISBN: 0465097618

Category: Computers

Page: 432

View: 9980

Author: Samantha Kleinberg

Publisher: Cambridge University Press

ISBN: 1107026482

Category: Computers

Page: 259

View: 6580

*An Introduction to Causal Inference*

Author: Paul R. Rosenbaum

Publisher: Harvard University Press

ISBN: 067497557X

Category: Mathematics

Page: 400

View: 6098

Author: K. J. Rothman

Publisher: Kenneth Rothman

ISBN: 9780917227035

Category: Medical

Page: 207

View: 4497

Author: Stephen L. Morgan,Christopher Winship

Publisher: Cambridge University Press

ISBN: 1107065070

Category: Mathematics

Page: 524

View: 8140

*An Introduction*

Author: Guido W. Imbens,Donald B. Rubin

Publisher: Cambridge University Press

ISBN: 1316094391

Category: Mathematics

Page: N.A

View: 644

*Applying Medical Anthropology*

Author: Michael Winkelman

Publisher: John Wiley & Sons

ISBN: 0470462612

Category: Medical

Page: 512

View: 2310

*Methods for Mediation and Interaction*

Author: Tyler VanderWeele,Tyler J.. VanderWeele

Publisher: Oxford University Press, USA

ISBN: 0199325871

Category: Psychology

Page: 706

View: 4053

*Networks of Plausible Inference*

Author: Judea Pearl

Publisher: Elsevier

ISBN: 0080514898

Category: Computers

Page: 552

View: 7742

*A Guide to Finding and Using Causes*

Author: Samantha Kleinberg

Publisher: "O'Reilly Media, Inc."

ISBN: 1491949627

Category: Mathematics

Page: 284

View: 2298

Author: N.A

Publisher: Elsevier

ISBN: 9780080930961

Category: Philosophy

Page: 1260

View: 2661

Author: Joseph Y. Halpern

Publisher: MIT Press

ISBN: 0262336626

Category: Philosophy

Page: 240

View: 3607

*Personal Reflections Inspired by the Last Words of Daniel Pearl*

Author: Judea Pearl,Ruth Pearl,Ehud Barak,Sylvia Boorstein,Edgar M. Bronfman,Alan Colmes,Kirk Douglas,Richard Dreyfuss,Kitty Dukakis,Dianne Feinstein,Tovah Feldshuh,Debbie Friedman,Ruth Bader Ginsburg,Nadine Gordimer,David Hartman,Moshe Katsav,Larry King,Francine Klagsbrun,Harold S. Kushner,Shia LaBeouf,Norman Lamm,Norman Lear,Julius Lester,Daniel Libeskind,Deborah E. Lipstadt

Publisher: Jewish Lights Publishing

ISBN: 1580232590

Category: Religion

Page: 262

View: 6631

*How to Get Beyond the Statistics Wars*

Author: Deborah G. Mayo

Publisher: Cambridge University Press

ISBN: 1107054133

Category: Mathematics

Page: 474

View: 7853

*Statistical Perspectives and Applications*

Author: Carlo Berzuini,Philip Dawid,Luisa Bernardinell

Publisher: John Wiley & Sons

ISBN: 1119941733

Category: Mathematics

Page: 416

View: 8343

*Moderation, Mediation and Spill-over*

Author: Guanglei Hong

Publisher: John Wiley & Sons

ISBN: 1119030609

Category: Mathematics

Page: 448

View: 5541