在本书中，人工智能领域的权威专家朱迪亚·珀尔及其同事领导的因果关系革命突破多年的迷雾，厘清了知识的本质，确立了因果关系研究在科学探索中的核心地位。
而因果关系科学真正重要的应用则体现在人工智能领域。作者在本书中回答的核心问题是：如何让智能机器像人一样思考？换言之，“强人工智能”可以实现吗？借助因果关系之梯的三个层级逐步深入地揭示因果推理的本质，并据此构建出相应的自动化处理工具和数学分析范式，作者给出了一个肯定的答案。作者认为，今天为我们所熟知的大部分机器学习技术，都建基于相关关系，而非因果关系。要实现强人工智能，乃至将智能机器转变为具有道德意识的有机体，我们就必须让机器学会问“为什么”，也就是要让机器学会因果推理，理解因果关系。或许，这正是我们能对准备接管我们未来生活的智能机器所做的最有意义的工作。

"Nate Silver's The Signal and the Noise is The Soul of a New Machine for the 21st century."
—Rachel Maddow, author of Drift
Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. The New York Times now publishes FiveThirtyEight.com, where Silver is one of the nation’s most influential political forecasters.
Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future.
In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good—or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary—and dangerous—science.
Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise.
With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver’s insights are an essential read.

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.

"This is that rarity, a useful book."--Warren Buffett Howard Marks, the chairman and cofounder of Oaktree Capital Management, is renowned for his insightful assessments of market opportunity and risk. After four decades spent ascending to the top of the investment management profession, he is today sought out by the world's leading value investors, and his client memos brim with insightful commentary and a time-tested, fundamental philosophy. Now for the first time, all readers can benefit from Marks's wisdom, concentrated into a single volume that speaks to both the amateur and seasoned investor. Informed by a lifetime of experience and study, The Most Important Thing explains the keys to successful investment and the pitfalls that can destroy capital or ruin a career. Utilizing passages from his memos to illustrate his ideas, Marks teaches by example, detailing the development of an investment philosophy that fully acknowledges the complexities of investing and the perils of the financial world. Brilliantly applying insight to today's volatile markets, Marks offers a volume that is part memoir, part creed, with a number of broad takeaways. Marks expounds on such concepts as "second-level thinking," the price/value relationship, patient opportunism, and defensive investing. Frankly and honestly assessing his own decisions--and occasional missteps--he provides valuable lessons for critical thinking, risk assessment, and investment strategy. Encouraging investors to be "contrarian," Marks wisely judges market cycles and achieves returns through aggressive yet measured action. Which element is the most essential? Successful investing requires thoughtful attention to many separate aspects, and each of Marks's subjects proves to be the most important thing.

本书是写给普通人的因果逻辑入门书，旨在帮助读者培养严谨的思维方式，在不借助任何专业知识的前提下，准确定位问题。主要内容包括：认识原因，对原因的理解和运用，如何只通过观察找到原因，大数据集与原因的关系，因果关系相关实验，如何利用因果关系来制定有效的干预措施，研究因果关系的意义。
本书适合所有对探究事件真相感兴趣的读者，无须统计学等专业背景。

Oxford Philosophical Texts Series Editor: John Cottingham The Oxford Philosophical Texts series consists of authoritative teaching editions of canonical texts in the history of philosophy from the ancient world down to modern times. Each volume provides a clear, well laid out text together with a comprehensive introduction by a leading specialist, giving the student detailed critical guidance on the intellectual context of the work and the structure and philosophical importance of the main arguments. Endnotes are supplied which provide further commentary on the arguments and explain unfamiliar references and terminology, and a full bibliography and index are also included. The series aims to build up a definitive corpus of key texts in the Western philosophical tradition, which will form a reliable and enduring resource for students and teachers alike. David Hume's aim in writing An Enquiry concerning Human Understanding (1748) was to introduce his philosophy to a European culture in which many educated people read original works of philosophy. He gives an elegant and accessible presentation of strikingly original and challenging views about the limited powers of human understanding, the attractions of scepticism, the compatibility of free will and determinism, and weaknesses in the foundations of religion. Hume's philosophy was highly controversial in the eighteenth century and remains so today. The text printed in this edition is that of the Clarendon critical edition of Hume's works. A substantial introduction by the editor explains the intellectual background to the work and surveys its main themes. The volume also includes detailed explanatory notes on the text, a glossary of terms, a full list of references, and a section of supplementary readings.

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.

运用当代分析哲学的许多资源，诸如模态逻辑、情境理论、非单调推理、非标准模型等，对亚里士多德四因说中的动力因和目的因进行了精密理论刻画。这两种因果形式都在当代哲学中扮演着越来越重要的核心角色，但用来理解因果作用的全面逻辑框架的发展却一直滞后。在语言哲学中，自索尔·克里普克在上世纪70年代的开拓性工作以来，关于指称与意义的因果理论不断成长；在与之并行的心智哲学领域，特别是关于感知、知识、意向和行动的理论中，因果理论也与日俱增。另外，在关于知识（普兰廷加）、意向性（米尼肯和德雷斯克）和伦理（新亚里士多德主义德行伦理）的目的论说明形式中，目的因或目的论研究也获得了复兴。

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.

《社会科学因果推断的理论基础》系统介绍了反事实的因果推论框架以及如何采用倾向值方法帮助社会科学经验研究者进行因果推论。除了基本的统计学原理之外，《社会科学因果推断的理论基础》回顾了倾向值方法的历史、发展及其对调查研究的意义，以及如何利用倾向值方法处理因果关系中的多类别性、中介性与异质性。除此之外，《社会科学因果推断的理论基础》还通过专门章节分析了比较个案研究中的综合控制个案方法以及因果推论过程中如何确定分析样本的样本量以及统计检定力。

The last decade has brought dramatic changes in the way that researchers analyze economic and financial time series. This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. "Time Series Analysis" fills an important need for a textbook that integrates economic theory, econometrics, and new results. The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.

Distinguished physicist examines emotive significance of time, time order of mechanics, time direction of thermodynamics and microstatistics, time direction of macrostatistics, and time of quantum physics. Analytic methods of scientific philosophy in investigation of probability, quantum mechanics, theory of relativity, causality. 1971 edition.

<b>A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.</b><br /><br />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.<br /><br />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.

Practical and professional, this text bridges the gap between how undergraduate econometrics has traditionally been taught and how empirical researchers actually think about and apply econometric methods. The text's unique approach reflects how econometric instruction has evolved from simply describing a set of abstract recipes to showing how econometrics can be used to empirically study questions across a variety of disciplines. The systematic approach, where assumptions are introduced only as they are needed to obtain a certain result, makes the material easier for students, and leads to better econometric practice. It is organised around the type of data being analysed - an approach that simplifies the exposition and allows a more careful discussion of assumptions. Packed with relevant applications and a wealth of interesting data sets, the text emphasises examples that have implications for policy or provide evidence for or against economic theories.

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.

The History of Econometric Ideas covers the period from the late nineteenth century to the middle of the twentieth century, illustrating how economists first learned to harness statistical methods to measure and test the "laws" of economics. Though scholarly, Dr. Morgan's book is very accessible; it does not require a high level of prior statistical knowledge, and will be of interest to practicing statisticians and economists.

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.