History And Causality

This volume investigates the different attitudes of historians and other social scientists to questions of causality. It argues that historical theorists after the linguistic turn have paid surprisingly little attention to causes in spite of the centrality of causation in many contemporary works of history.

This pioneering work is the first to trace how our understanding of the causes of human behavior has changed radically over the course of European and American cultural history since 1830. Focusing on the act of murder, as documented vividly by more than a hundred novels including Crime and Punishment, An American Tragedy, The Trial, and Lolita, Stephen Kern devotes each chapter of A Cultural History of Causality to examining a specific causal factor or motive for murder--ancestry, childhood, language, sexuality, emotion, mind, society, and ideology. In addition to drawing on particular novels, each chapter considers the sciences (genetics, endocrinology, physiology, neuroscience) and systems of thought (psychoanalysis, linguistics, sociology, forensic psychiatry, and existential philosophy) most germane to each causal factor or motive. Kern identifies five shifts in thinking about causality, shifts toward increasing specificity, multiplicity, complexity, probability, and uncertainty. He argues that the more researchers learned about the causes of human behavior, the more they realized how much more there was to know and how little they knew about what they thought they knew. The book closes by considering the revolutionary impact of quantum theory, which, though it influenced novelists only marginally, shattered the model of causal understanding that had dominated Western thought since the seventeenth century. Others have addressed changing ideas about causality in specific areas, but no one has tackled a broad cultural history of this concept as does Stephen Kern in this engagingly written and lucidly argued book.

Presents a new approach to causal inference and explanation, addressing both the timing and complexity of relationships.

What types of entities qualify as causes and effects? What is the relationship between cause and effect? How are causal claims to be assessed? The first question deals with the structure of the world; the second is about theories that interpret the relationship of causes to effects; while the third has to do with proper procedure in science and everyday life. This volume is a wide-ranging history of answers that have been given to these three questions, and their relationship to scientific understanding.Losee presents a number of theories of causality within a historical survey that emphasizes the interrelationship between these theories and developments in science. His analysis displays the strengths and weaknesses of these theories so as to contribute to our present understanding of causal relatedness.Among the positions discussed are those of Aristotle, Hume, Kant, Mill, Salmon, Lewis, and Woodward. Losee's analysis displays the strengths and weaknesses of theories that identify causal relatedness with regularity of sequence, probability increase, energy transfer, exchange of a conserved quantity, counterfactual dependence, and inferability.These theories are judged, in part,by their ability to resolvedifficulties posed by instances of overdetermination,causation by omission, preventive causation, and causation by disconnection. Since applications of the theories to these instances disagree, a strategy of employing multiple concepts of causation is examined.Theories of Causality also describes the particular difficulties for causal analysis posed by quantum mechanics. One such difficulty is the prohibition against combining a causal analysis of a quantum process with a spatio-temporal description of that process.

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.

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 ...

Why is understanding causation so important in philosophy and the sciences? Should causation be defined in terms of probability? Whilst causation plays a major role in theories and concepts of medicine, little attempt has been made to connect causation and probability with medicine itself. Causality, Probability, and Medicine is one of the first books to apply philosophical reasoning about causality to important topics and debates in medicine. Donald Gillies provides a thorough introduction to and assessment of competing theories of causality in philosophy, including action-related theories, causality and mechanisms, and causality and probability. Throughout the book he applies them to important discoveries and theories within medicine, such as germ theory; tuberculosis and cholera; smoking and heart disease; the first ever randomized controlled trial designed to test the treatment of tuberculosis; the growing area of philosophy of evidence-based medicine; and philosophy of epidemiology. This book will be of great interest to students and researchers in philosophy of science and philosophy of medicine, as well as those working in medicine, nursing and related health disciplines where a working knowledge of causality and probability is required.