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Thinking Things Through, second edition
The second edition of a unique introductory text, offering an account of the logical tradition in philosophy and its influence on contemporary scientific disciplines. Thinking Things Through offers a broad, historical, and rigorous introduction to the logical tradition in philosophy and its contemporary significance. It is unique among introductory philosophy texts in that it considers both the historical development and modern fruition of a few central questions. It traces the influence of philosophical ideas and arguments on modern logic, statistics, decision theory, computer science, cognitive science, and public policy. The text offers an account of the history of speculation and argumen...
Causation, Prediction, and Search
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. ...
Theory and Evidence
The Description for this book, Theory and Evidence, will be forthcoming.
Galileo in Pittsburgh
What did the trial of Galileo share with the trial for fraud of the foremost investigator of the effects of lead exposure on children’s intelligence? In the title essay of this rollicking collection on science and education, Clark Glymour argues that fundamentally both were disputes over what methods are legitimate and authoritative. From testing the expertise of NASA scientists to discovering where software goes to die to turning educational research upside down, Glymour’s reports from the front lines of science and education read like a blend of Rachel Carson and Hunter S. Thompson. Contrarian and original, he criticizes the statistical arguments against Teach for America, argues for t...
Actual Causality
A new approach for defining causality and such related notions as degree of responsibility, degrees of blame, and causal explanation. 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, an...
Testing Scientific Theories
Testing Scientific Theories was first published in 1984. Minnesota Archive Editions uses digital technology to make long-unavailable books once again accessible, and are published unaltered from the original University of Minnesota Press editions. Since much of a scientist's work consists of constructing arguments to show how experiments and observation bear on a particular theory, the methodologies of theory testing and their philosophical underpinnings are of vital concern to philosophers of science. Confirmation of scientific theories is the topic of Clark Glymour's important book Theory and Evidence,published in 1980. His negative thesis is that the two most widely discussed accounts of ...
Causal Learning
Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories of concepts and cognitive development, and moreover, the causal learning mechanisms it has uncovered go dramatically beyond the traditional mechanisms of both nativist theories, such as modularity theories, and empiricist ones, such as association or connectionism.
Android Epistemology
"Were they reborn into a modern university, Plato and Aristotle and Leibniz would most suitably take up appointments in the department of computer science." Epistemology has traditionally been the study of human knowledge and rational change of human belief. Android epistemology is the exploration of the space of possible machines and their capacities for knowledge, beliefs, attitudes, desires, and for action in accord with their mental states. From the perspective of android epistemology, artificial intelligence and computational cognitive psychology form a unified endeavor: artificial intelligence explores any possible way of engineering machines with intelligent features, while cognitive ...
The Mind's Arrows
This title provides an introduction to assumptions, algorithms, and techniques of causal Bayes nets and graphical causal models in the context of psychological examples. It demonstrates their potential as a powerful tool for guiding experimental inquiry.
Causal Inference in Statistics
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 sit...
