The role of causal reasoning in understanding Simpson's paradox, Lord's paradox, and the suppression effect: covariate selection in the analysis of observational studies

Written by one of the pre-eminent 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, philosophy, cognitive science, and the health and social sciences. 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, artifical intelligence, business, epidemiology, social science and economics. Students in these areas will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts that traditional texts have tended to evade or make unduly complicated. This book will be of interest to professionals and students in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable. Professor of Computer Science at the UCLA, Judea Pearl is the winner of the 2008 Benjamin Franklin Award in Computers and Cognitive Science 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. The book will open the way for including causal analysis in the standard curricula of statistics, artificial intelligence, business, epidemiology, social sciences, and economics. Students in these fields will find natural models, simple inferential procedures, and precise mathematical definitions of causal concepts that traditional texts have evaded or made unduly complicated. The first edition of Causality has led to a paradigmatic change in the way that causality is treated in statistics, philosophy, computer science, social science, and economics. Cited in more than 5,000 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 interests to students and professionals in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable. 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. Tu __et al__ present an analysis of the equivalence of three paradoxes, namely, Simpson's, Lord's, and the suppression phenomena. They conclude that all three simply reiterate the occurrence of a change in the association of any two variables when a third variable is statistically controlled for. This is not surprising because reversal or change in magnitude is common in conditional analysis. At the heart of the phenomenon of change in magnitude, with or without reversal of effect estimate, is the question of which to use: the unadjusted (combined table) or adjusted (sub-table) estimate. Hence, Simpson's paradox and related phenomena are a problem of covariate selection and adjustment (when to adjust or not) in the causal analysis of non-experimental data. It cannot be overemphasized that although these paradoxes reveal the perils of using statistical criteria to guide causal analysis, they hold neither the explanations of the phenomenon they depict nor the pointers on how to avoid them. The explanations and solutions lie in causal reasoning which relies on background knowledge, not statistical criteria. 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 readersʼ questions, and offers a panoramic view of recent advances in this field of research."--Publisher description Written by one of the preeminent scientists 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 diverse fields. Causality will be of interest to professionals and students in the fields of statistics, artificial intelligence, philosophy, cognitive science, and the health and social sciences; and, more generally, anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech. Introduction To Probabilities, Graphs, And Causal Models -- Theory Of Inferred Causation -- Causal Diagrams And The Identification Of Causal Effects -- Actions, Plans, And Direct Effects -- Causality And Structural Models In Social Science And Economics -- Simpson's Paradox, Confounding, And Collapsibility -- Logic Of Structure-based Counterfactuals -- Imperfect Experiments: Bounding Effects And Counterfactuals -- Probability Of Causation: Interpretation And Identification -- The Actual Cause -- Reflections, Elaborations, And Discussions With Readers -- The Art And Science Of Cause And Effect. Judea Pearl. Includes Bibliographical References (p. 429-451) And Indexes. Introduction To Probabilities, Graphs, And Causal Models -- Theory Of Inferred Causation -- Causal Diagrams And The Identification Of Causal Effects -- Actions, Plans, And Direct Effects -- Causality And Structural Models In Social Science And Economics -- Simpson's Paradox, Confounding, And Collapsibility -- Logic Of Structure-based Counterfactuals -- Imperfect Experiments: Bounding Effects And Counterfactuals -- Probability Of Causation: Interpretation And Identification -- Actual Cause. Judea Pearl. Includes Bibliographical References (p. 359-373) And Indexes. Exploring data Modeling distributions of data Describing relationships Designing studies Cumulative AP practice test Probability: what are the chances? Random variables Sampling distributions Cumulative AP practice test Estimating with confidence Testing a claim Comparing two populations or groups Cumulative AP practice test Inference for distributions of categorical data More about regression Cumulative AP practice test Analysis of variance Multiple linear regression Logistic regression The Practice of Statistics long stands as the only high school statistics textbook that directly reflects the College Board course description for AP* Statistics. Combining the data analysis approach with the power of technology, innovative pedagogy, and a number of new features, the fourth edition will provide you and your students with the most effective text for learning statistics and succeeding on the AP* Exam. - Publisher Combining a data analysis approach with the power of technology, innovative pedagogy, and a number of new features, the fourth edition is an impressively effective text for learning statistics. Causality connotes lawlike necessity, whereas probabilities connote exceptionality, doubt, and lack of regularity. Books had been well loved. Binding is lose and some pages maybe as will. still very useable