Everything we do relies on causation. We eat and drink because this causes us to stay alive. Courts tell us who causes crimes, criminology tell us what causes people to commit them. D.H. Mellor shows us that to understand the world and our lives we must understand causation. The Facts of Causation, now available in paperback, is essential reading for students and for anyone interested in reading one of the ground-breaking theories in metaphysics. We cannot understand the world and our place in it without understanding causation. Yet a complete account of the nature and implications of causation does not exist. D.H Mellor's new book is that account.

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.

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.

One philosophical approach to causation sees counterfactual dependence as the key to the explanation of causal facts: for example, events c (the cause) and e (the effect) both occur, but had c not occurred, e would not have occurred either. The counterfactual analysis of causation became a focus of philosophical debate after the 1973 publication of the late David Lewis's groundbreaking paper, "Causation," which argues against the previously accepted "regularity" analysis and in favor of what he called the "promising alternative" of the counterfactual analysis. Thirty years after Lewis's paper, this book brings together some of the most important recent work connecting—or, in some cases, disputing the connection between—counterfactuals and causation, including the complete version of Lewis's Whitehead lectures, "Causation as Influence," a major reworking of his original paper. Also included is a more recent essay by Lewis, "Void and Object," on causation by omission. Several of the essays first appeared in a special issue of the Journal of Philosophy, but most, including the unabridged version of "Causation as Influence," are published for the first time or in updated forms. Other topics considered include the "trumping" of one event over another in determining causation; de facto dependence; challenges to the transitivity of causation; the possibility that entities other than events are the fundamental causal relata; the distinction between dependence and production in accounts of causation; the distinction between causation and causal explanation; the context-dependence of causation; probabilistic analyses of causation; and a singularist theory of causation.

Carolina Sartorio argues that only the actual causes of our behaviour matter to our freedom. Although this simple view of freedom clashes with most theories of responsibility, including the most prominent 'actual sequence' theories currently on offer, Sartorio argues for its truth. The key, she claims, lies in a correct understanding of the role played by causation in a view of that kind. Causation has some important features that make it a responsibility-grounding relation, and this to the success of the view. Also, when agents act freely, the actual causes are richer than they appear to be at first sight; in particular, they reflect the agents' sensitivity to reasons, where this includes both the existence of actual reasons and the absence of other (counterfactual) reasons. So acting freely requires more causes and quite complex causes, as opposed to fewer causes and simpler causes, and is compatible with those causes being deterministic. The book connects two different debates, the one on causation and the one on the problem of free will, in new and illuminating ways.

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.

When you light a match it is the striking of it which causes the lighting; the presence of oxygen in the room is a background condition to the lighting. But in virtue of what is the striking a cause while the presence of oxygen is a background condition? When a fragile glass breaks it manifests a disposition to break when struck; however, not everything that breaks manifests this disposition. So under what conditions does something, in breaking, manifest fragility? After some therapy a man might stop being irascible and he might lose the disposition to become angry at the slightest provocation. If he does then he will have lost the disposition after an "internal" change. Can someone lose, or gain, a disposition merely as a result of a change in its external circumstances? Facts about the structure of society can, it seems, explain other facts. But how do they do it? Are there different kinds of structural explanations? Many things are said to be causes: a rock, when we say that the rock caused the window to break, and an event, when we say that the striking of the window caused its breakage. Which kind of causation - causation by events, or causation by things - is more basic? In Causation, Explanation, and the Metaphysics of Aspect, Bradford Skow defends answers to these questions. His answers rely on a pair of connected distinctions: first is the distinction between acting, or doing something, and not acting; second is the distinction between situations in which an event happens, and situations in which instead something is in some state. The first distinction is used to draw the second: an event happens if and only if something does something.

Not all scientific explanations work by describing causal connections between events or the world's overall causal structure. In addition, mathematicians regard some proofs as explaining why the theorems being proved do in fact hold. This book proposes new philosophical accounts of many kinds of non-causal explanations in science and mathematics.

We explain what people think and do by citing their reasons, but how do such explanations work, and what do they tell us about the nature of reality? Contemporary efforts to address these questions are often motivated by the worry that our ordinary conception of rationality contains a kernel of supernaturalism-a ghostly presence that meditates on sensory messages and orchestrates behavior on the basis of its ethereal calculations. In shunning this otherworldly conception, contemporary philosophers have focused on the project of "naturalizing" the mind, viewing it as a kind of machine that converts sensory input and bodily impulse into thought and action. Eric Marcus rejects this choice between physicalism and supernaturalism as false and defends a third way. He argues that philosophers have failed to take seriously the idea that rational explanations postulate a distinctive sort of causation-rational causation. Rational explanations do not reveal the same sorts of causal connections that explanations in the natural sciences do. Rather, rational causation draws on the theoretical and practical inferential abilities of human beings. Marcus defends this position against a wide array of physicalist arguments that have captivated philosophers of mind for decades. Along the way he provides novel views on, for example, the difference between rational and nonrational animals and the distinction between states and events.

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.