From Cause to Causation presents both a critical analysis of C.S. Peirce's conception of causation, and a novel approach to causation, based upon the semeiotic of Peirce. The book begins with a review of the history of causation, and with a critical discussion of contemporary theories of the concept of `cause'. The author uncovers a number of inadequacies in the received views of causation, and discusses their historical roots. He makes a distinction between "causality", which is the relation between cause and effect, and causation, which is the production of a certain effect. He argues that, by focusing on causality, the contemporary theories fatally neglect the more fundamental problem of causation. The author successively discusses Peirce's theories of final causation, natural classes, semeiotic, and semeiotic causation. Finally, he uses Peirce's semeiotic to develop a new approach to causation, which relates causation to our experience of signs.

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.

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.

Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. 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, artificial intelligence ...

This volume is a collection of new essays by specialists that trace the concept of efficient causation from its discovery (or invention) in Ancient Greece, through its development in late antiquity, the medieval period, and modern philosophy, to its use in contemporary metaphysics and philosophy of science.

The application of causal inference methods is growing exponentially in fields that deal with observational data. Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. With a wide range of detailed, worked examples using real epidemiologic data as well as software for replicating the analyses, the text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data.

Philosophers have long been fascinated by the connection between cause and effect: are 'causes' things we can experience, or are they concepts provided by our minds? The study of causation goes back to Aristotle, but resurged with David Hume and Immanuel Kant, and is now one of the most important topics in metaphysics. Most of the recent work done in this area has attempted to place causation in a deterministic, scientific, worldview. But what about the unpredictable and chancey world we actually live in: can one theory of causation cover all instances of cause and effect? Cause and Chance: Causation in an Indeterministic World is a collection of specially written papers by world-class metaphysicians. Its focus is the problem facing the 'reductionist' approach to causation: the attempt to cover all types of causation, deterministic and indeterministic, with one basic theory. Contributors: Stephen Barker, Helen Beebee, Phil Dowe, Dorothy Edgington, Doug Ehring, Chris Hitchcock, Igal Kwart, Paul Noordhof, Murali Ramachandran and Michael Tooley.

An accessible, contemporary introduction to the methods for determining cause and effect in the Social Sciences “Causation versus correlation has been the basis of arguments—economic and otherwise—since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It’s rare that a book prompts readers to expand their outlook; this one did for me.”—Marvin Young (Young MC) Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages.

This book explores the role of causal constraints in science, shifting our attention from causal relations between individual events--the focus of most philosophical treatments of causation—to a broad family of concepts and principles generating constraints on possible change. Yemima Ben-Menahem looks at determinism, locality, stability, symmetry principles, conservation laws, and the principle of least action—causal constraints that serve to distinguish events and processes that our best scientific theories mandate or allow from those they rule out. Ben-Menahem's approach reveals that causation is just as relevant to explaining why certain events fail to occur as it is to explaining events that do occur. She investigates the conceptual differences between, and interrelations of, members of the causal family, thereby clarifying problems at the heart of the philosophy of science. Ben-Menahem argues that the distinction between determinism and stability is pertinent to the philosophy of history and the foundations of statistical mechanics, and that the interplay of determinism and locality is crucial for understanding quantum mechanics. Providing historical perspective, she traces the causal constraints of contemporary science to traditional intuitions about causation, and demonstrates how the teleological appearance of some constraints is explained away in current scientific theories such as quantum mechanics. Causation in Science represents a bold challenge to both causal eliminativism and causal reductionism—the notions that causation has no place in science and that higher-level causal claims are reducible to the causal claims of fundamental physics.

Causation is the most fundamental connection in the universe. Without it, there would be no science or technology. There would be no moral responsibility either, as none of our thoughts would be connected with our actions and none of our actions with any consequences. Nor would we have a system of law because blame resides only in someone having caused injury or damage. Any intervention we make in the world around us is premised on there being causal connections that are, to a degree, predictable. It is causation that is at the basis of prediction and also explanation. This Very Short Introduction introduces the key theories of causation and also the surrounding debates and controversies. Do causes produce their effects by guaranteeing them? Do causes have to precede their effects? Can causation be reduced to the forces of physics? And are we right to think of causation as one single thing at all? ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.