This paper defends a counterfactual account of explanation, according to which successful explanation requires tracing patterns of counterfactual dependence of a special sort, involving what I call active counterfactuals. Explanations having this feature must appeal to generalizations that are invariant--stable under certain sorts of changes. These ideas are illustrated by examples drawn from physics and econometrics.

Probabilistic accounts of causality have long had trouble with ‘spurious’ evidential correlations. Such correlations are also central to the case for causal decision theory—the argument that evidential decision theory is inadequate to cope with certain sorts of decision problem. However, there are now several strong defences of the evidential theory. Here I present what I regard as the best defence, and apply it to the probabilistic approach to causality. I argue that provided a probabilistic theory appeals to the notions of (...) agency and effective strategy, it can avoid the problem of spurious causes. I show that such an appeal has other advantages; and argue that it is not illegitimate, even for a causal realist. (shrink)

This book on the philosophy of science argues for an empiricism, opposed to the tradition of David Hume, in which singular rather than general causal claims are primary; causal laws express facts about singular causes whereas the general causal claims of science are ascriptions of capacities or causal powers, capacities to make things happen. Taking science as measurement, Cartwright argues that capacities are necessary for science and that these can be measured, provided suitable conditions are met. There are case studies (...) from both econometrics and quantum mechanics. (shrink)

This book, by one of the pre-eminent philosophers of science writing today, offers the most comprehensive account available of causal asymmetries. Causation is asymmetrical in many different ways. Causes precede effects; explanations cite causes not effects. Agents use causes to manipulate their effects; they don't use effects to manipulate their causes. Effects of a common cause are correlated; causes of a common effect are not. This book explains why a relationship that is asymmetrical in one of these regards is asymmetrical (...) in the others. Hausman discovers surprising hidden connections between theories of causation and traces them all to an asymmetry of independence. This is a major book for philosophers of science that will also prove insightful to economists and statisticians. (shrink)

This book concentrates on research done during the last twenty years on the philosophy of quantum mechanics. In particular, the author focuses on three major issues: whether quantum mechanics is an incomplete theory, whether it is non-local, and whether it can be interpreted realistically. Much of the book is concerned with distinguishing various senses in which these questions can be taken, and assessing the bewildering variety of answers philosophers and physicists have given up to now. The book is self-contained in (...) that it presents the necessary parts of the mathematical formalism of quantum mechanics and also covers other interpretative topics, such as the problem of measurement and the uncertainty relations. A considerable portion of the book is based on original arguments presented by the author in lectures and research papers over the past ten years. However, this material is integrated with a broad coverage of most of the recent research in the field, so as to provide a balanced introduction to the whole subject. (shrink)

Aiming to unravel the mystery of quantum mechanics, this book is concerned with questions about action-at-a-distance, holism, and whether quantum mechanics gives a complete account of microphysical reality. With rigorous arguments and clear thinking, the author provides an introduction to the philosophy of physics.

Clark Glymour, Richard Scheines, Peter Spirtes and Kevin Kelly. Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.

Causality in Macroeconomics examines causality while taking macroeconomics seriously. A pragmatic and realistic philosophy is joined to a macroeconomic foundation that refines Herbert Simon's well-known work on causal order to make a case for a structural approach to causality. The structural approach is used to understand modern rational expectations models, regime switching models, Granger causality, vector autoregressions, the Lucas critique, and concept exogeneity. Techniques of causal inference based on patterns of stability and instability in the face of identified regime changes (...) are developed and illustrated in two empirical case studies. (shrink)