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Causal questions are relevant to all sciences and social sciences, yet how we discover causal connections is no easy matter. Indeed, the choice of methods concerns the correct norms for the empirical study of the world. In this text, two experts on causation relate philosophical theory to scientific practice and propose nine new norms of discovery.
Causation is the main foundation upon which the possibility of science rests. Without causation, there would be no scientific understanding, explanation, prediction, nor application in new technologies. How we discover causal connections is no easy matter, however. Causation often lies hidden from view and it is vital that we adopt the right methods for uncovering it. The choice of methods will inevitably reflect what one takes causation to be, making an accurate account of causation an even more pressing matter. This enquiry informs the correct norms for an empirical study of the world. In Causation in Science and the Methods of Scientific Discovery, Rani Lill Anjum and Stephen Mumford propose nine new norms of scientific discovery. A number of existing methodological and philosophical orthodoxies are challenged as they argue that progress in science is being held back by an overly simplistic philosophy of causation.
This volume addresses fundamental issues in the philosophy of science in the context of two most intriguing fields: biology and economics. Written by authorities and experts in the philosophy of biology and economics, Mechanism and Causality in Biology and Economics provides a structured study of the concepts of mechanism and causality in these disciplines and draws careful juxtapositions between philosophical apparatus and scientific practice. By exploring the issues that are most salient to the contemporary philosophies of biology and economics and by presenting comparative analyses, the book serves as a platform not only for gaining mutual understanding between scientists and philosophers of the life sciences and those of the social sciences, but also for sharing interdisciplinary research that combines both philosophical concepts in both fields. The book begins by defining the concepts of mechanism and causality in biology and economics, respectively. The second and third parts investigate philosophical perspectives of various causal and mechanistic issues in scientific practice in the two fields. These two sections include chapters on causal issues in the theory of evolution; experiments and scientific discovery; representation of causal relations and mechanism by models in economics. The concluding section presents interdisciplinary studies of various topics concerning extrapolation of life sciences and social sciences, including chapters on the philosophical investigation of conjoining biological and economic analyses with, respectively, demography, medicine and sociology.
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.
Causal questions are relevant to all sciences and social sciences, yet how we discover causal connections is no easy matter. Indeed, the choice of methods concerns the correct norms for the empirical study of the world. In this text, two experts on causation relate philosophical theory to scientific practice and propose nine new norms of discovery.
This collection brings together the latest new work within an emerging philosophical discipline: the metaphysics of science. A new definition of this line of philosophical enquiry is developed, and leading academics offer original essays on four key topics at the heart of the subject—laws, causation, natural kinds, and emergence.
This book goes beyond the truism that 'correlation does not imply causation' and explores the logical and methodological relationships between correlation and causation. It presents a series of statistical methods that can test, and potentially discover, cause-effect relationships between variables in situations in which it is not possible to conduct randomised or experimentally controlled experiments. Many of these methods are quite new and most are generally unknown to biologists. In addition to describing how to conduct these statistical tests, the book also puts the methods into historical context and explains when they can and cannot justifiably be used to test or discover causal claims. Written in a conversational style that minimises technical jargon, the book is aimed at practising biologists and advanced students, and assumes only a very basic knowledge of introductory statistics.
First edition published in 2002. Second edition published in 2008.
This innovative research design text will help you make informed choices when carrying out your research project. Covering both qualitative and quantitative approaches, and with examples drawn from a wide range of social science disciplines, the authors explain what is at stake when choosing a research design, and discuss the trade-offs that researchers have to make when considering issues such as: - causality - categories and classification - heterogeneity - interdependence - time This book will appeal to students and researchers looking for an in-depth understanding of research design issues to help them design their projects in a thoughtful and responsible way.
Why do ideas of how mechanisms relate to causality and probability differ so much across the sciences? Can progress in understanding the tools of causal inference in some sciences lead to progress in others? This book tackles these questions and others concerning the use of causality in the sciences.