Judea Pearl – författare

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.

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.

How the study of causality revolutionized science and the world

"Correlation does not imply causation." This mantra has been invoked by scientists for decades, and has led to a virtual prohibition on causal talk. But today, that taboo is dead. The causal revolution, sparked by Judea Pearl and his colleagues, has cut through a century of confusion and placed 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.

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.

Being Jewish. What does it mean—today—and for the future? Listen in as Jews of all backgrounds reflect, argue, and imagine.

When Wall Street Journal reporter Daniel Pearl was brutally murdered in Pakistan, many Jews were particularly touched by his last words affirming his Jewish identity. Many were moved to reflect on or analyze their feelings toward their lives as Jews.

The saying "two Jews, three opinions" well reflects the Jewish community''s broad range of views on any topic. I Am Jewish captures this richness of interpretation and inspires Jewish people of all backgrounds to reflect upon and take pride in their identity.

Contributions, ranging from major essays to a paragraph or a sentence, come from adults as well as young people in the form of personal feelings, statements of theology, life stories, and historical reflections. Despite the diversity, common denominators shine through clearly and distinctly.

Contributors include:Ehud Barak • Sylvia Boorstein • Edgar M. Bronfman • Alan Colmes • Alan Dershowitz • Kirk Douglas • Richard Dreyfuss • Kitty Dukakis • Dianne Feinstein • Tovah Feldshuh • Debbie Friedman • Milton Friedman • Thomas L. Friedman • Ruth Bader Ginsburg • Nadine Gordimer • David Hartman • Moshe Katsav • Larry King • Francine Klagsbrun • Harold Kushner • Lawrence Kushner • Shia LaBeouf • Norman Lamm • Norman Lear • Julius Lester • Bernard-Henri Lévy • Bernard Lewis • Daniel Libeskind • Joe Lieberman • Deborah E. Lipstadt • Joshua Malina • Michael Medved • Ruth W. Messinger • Amos Oz • Cynthia Ozick • Shimon Peres • Martin Peretz • Dennis Prager • Anne Roiphe • Sandy Eisenberg Sasso • Vidal Sassoon • Zalman M. Schachter-Shalomi • Daniel Schorr • Harold M. Schulweis • Lynn Schusterman • Natan Sharansky • Gary Shteyngart • Sarah Silverman • Michael H. Steinhardt • Kerri Strug • Lawrence H. Summers • Mike Wallace • Elie Wiesel • Leon Wieseltier • Sherwin T. Wine • Ruth R. Wisse • Peter Yarrow • A. B. Yehoshua • Eric H. Yoffie