Woodward's long awaited book is an attempt to construct a comprehensive account of causation explanation that applies to a wide variety of causal and explanatory claims in different areas of science and everyday life. The book engages some of the relevant literature from other disciplines, as Woodward weaves together examples, counterexamples, criticisms, defenses, objections, and replies into a convincing defense of the core of his theory, which is that we can analyze causation by appeal to the notion of manipulation.

Meek and Glymour use the graphical approach to causal modeling to argue that one and the same norm of rational choice can be used to deliver both causal-decision-theoretic verdicts and evidential-decision-theoretic verdicts. Specifically, they argue that if an agent maximizes conditional expected utility, then the agent will follow the causal decision theorist’s advice when she represents herself as intervening, and will follow the evidential decision theorist’s advice when she represents herself as not intervening. Since Meek and Glymour take no stand (...) on whether agents should represent themselves as intervening, they provide more general advice than standard causal decision theorists and evidential decision theorists. But I argue here that even Meek and Glymour’s advice is not sufficiently general. This is because their advice is not sensitive to the distinct ways in which agents can fail to intervene, and there are decision-making contexts in which agents can reasonably have non-extreme confidence that they are intervening. I then show that the most natural extension of Meek and Glymour’s framework fails, but offer a generalization of my “Interventionist Decision Theory” that does not suffer from the same problems. (shrink)

In “A Subjectivist’s Guide to Objective Chance,” David Lewis says that he is “led to wonder whether anyone but a subjectivist is in a position to understand objective chance.” The present essay aims to motivate this same Lewisean attitude, and a similar degree of modest subjectivism, with respect to objective causation. The essay begins with Newcomb problems, which turn on an apparent tension between two principles of choice: roughly, a principle sensitive to the causal features of the relevant situation, and (...) a principle sensitive only to evidential factors. Two-boxers give priority to causal beliefs, and one-boxers to evidential beliefs. The essay notes that a similar issue can arise when the modality in question is chance, rather than causation. In this case, the conflict is between decision rules based on credences guided solely by chances, and rules based on credences guided by other sorts of probabilistic evidence. Far from excluding cases of the latter kind, Lewis’s Principal Principle explicitly allows for them, in the form of the caveat that credences should follow beliefs about chances only in the absence of “inadmissible evidence.” The essay then exhibits a tension in Lewis’s views on these two matters, by presenting a class of decision problems—some of them themselves Newcomb problems—in which Lewis’s view of the relevance of inadmissible evidence seems in tension with his causal decision theory. It offers a diagnosis for this dilemma and proposes a remedy, based on an extension of a proposal due to Ned Hall and others from the case of chance to that of causation. The remedy suggests a new view of the relation between causal decision theory and evidential decision theory, namely, that they stand to each other much as chance stands to credence, being objective and subjective faces of the same practical coin. This has much the same metaphysical benefits as Lewis’s own view of chance and also throws interesting new light on Newcomb problems, providing an irenic resolution of the apparent disagreement between causal and evidential decision rules. (shrink)

Proponents of causal decision theories argue that classical Bayesian decision theory (BDT) gives the wrong advice in certain types of cases, of which the clearest and commonest are the medical Newcomb problems. I defend BDT, invoking a familiar principle of statistical inference to show that in such cases a free agent cannot take the contemplated action to be probabilistically relevant to its causes (so that BDT gives the right answer). I argue that my defence does better than those of Ellery (...) Eells and Richard Jeffrey; and that it applies, where necessary, to other types of Newcomb problem. (shrink)

We consider the dispute between causal decision theorists and evidential decision theorists over Newcomb-like problems. We introduce a framework relating causation and directed graphs developed by Spirtes et al. (1993) and evaluate several arguments in this context. We argue that much of the debate between the two camps is misplaced; the disputes turn on the distinction between conditioning on an event E as against conditioning on an event I which is an action to bring about E. We give the essential (...) machinery for calculating the effect of an intervention and consider recent work which extends the basic account given here to the case where causal Knowledge is incomplete. (shrink)

This is the second volume of philosophical essays by one of the most innovative and influential philosophers now writing in English. Containing thirteen papers in all, the book includes both new essays and previously published papers, some of them with extensive new postscripts reflecting Lewis's current thinking. The papers in Volume II focus on causation and several other closely related topics, including counterfactual and indicative conditionals, the direction of time, subjective and objective probability, causation, explanation, perception, free will, and rational (...) decision. Throughout, Lewis analyzes global features of the world in such a way as to show that they might turn out to supervene on the spatiotemporal arrangement of local qualities. (shrink)

Newcomb's problem and similar cases show the need to incorporate causal distinctions into the theory of rational decision; the usual noncausal decision theory, though simpler, does not always give the right answers. I give my own version of causal decision theory, compare it with versions offered by several other authors, and suggest that the versions have more in common than meets the eye.

This book defends the view that any adequate account of rational decision making must take a decision maker's beliefs about causal relations into account. The early chapters of the book introduce the non-specialist to the rudiments of expected utility theory. The major technical advance offered by the book is a 'representation theorem' that shows that both causal decision theory and its main rival, Richard Jeffrey's logic of decision, are both instances of a more general conditional decision theory. The book solves (...) a long-standing problem for Jeffrey's theory by showing for the first time how to obtain a unique utility and probability representation for preferences and judgements of comparative likelihood. The book also contains a major new discussion of what it means to suppose that some event occurs or that some proposition is true. The most complete and robust defence of causal decision theory available. (shrink)

Andy Egan has recently produced a set of alleged counterexamples to causal decision theory in which agents are forced to decide among causally unratifiable options, thereby making choices they know they will regret. I show that, far from being counterexamples, CDT gets Egan's cases exactly right. Egan thinks otherwise because he has misapplied CDT by requiring agents to make binding choices before they have processed all available information about the causal consequences of their acts. I elucidate CDT in a way (...) that makes it clear where Egan goes wrong, and which explains why his examples pose no threat to the theory. My approach has similarities to a modification of CDT proposed by Frank Arntzenius, but it differs in the significance that it assigns to potential regrets. I maintain, contrary to Arntzenius, that an agent facing Egan's decisions can rationally choose actions that she knows she will later regret. All rationality demands of agents it that they maximize unconditional causal expected utility from an epistemic perspective that accurately reflects all the available evidence about what their acts are likely to cause. This yields correct answers even in outlandish cases in which one is sure to regret whatever one does. (shrink)

"[This book] proposes new foundations for the Bayesian principle of rational action, and goes on to develop a new logic of desirability and probabtility."—Frederic Schick, _Journal of Philosophy_.

Many philosophers (myself included) have been converted to causal decision theory by something like the following line of argument: Evidential decision theory endorses irrational courses of action in a range of examples, and endorses “an irrational policy of managing the news”. These are fatal problems for evidential decision theory. Causal decision theory delivers the right results in the troublesome examples, and does not endorse this kind of irrational news-managing. So we should give up evidential decision theory, and be causal decision (...) theorists instead. Unfortunately, causal decision theory has its own family of problematic examples for which it endorses irrational courses of action, and its own irrational policy that it is committed to endorsing. These are, I think, fatal problems for causal decision theory. I wish that I had another theory to offer in its place. (shrink)

The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of ‘hard' and ‘soft' interventions and discuss what they can contribute to causal discovery. We also describe how the choice of the optimal intervention(s) depends heavily on the (...) particular experimental setup and the assumptions that can be made. ‡The first author is funded by the Causal Learning Collaborative Initiative supported by the James S. McDonnell Foundation. Many aspects of this paper were inspired by discussions with members of the collaborative. †To contact the authors, please write to: Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213; e-mail: [email protected] and [email protected]. (shrink)

First published in 1982, Ellery Eells' original work on rational decision making had extensive implications for probability theorists, economists, statisticians and psychologists concerned with decision making and the employment of Bayesian principles. His analysis of the philosophical and psychological significance of Bayesian decision theories, causal decision theories and Newcomb's paradox continues to be influential in philosophy of science. His book is now revived for a new generation of readers and presented in a fresh twenty-first-century series livery, including a specially commissioned (...) preface written by Brian Skyrms, illuminating its continuing importance and relevance to philosophical enquiry. (shrink)

Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections (...) and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence and systems from The Cognitive Science Society. (shrink)

In past years, the traditional Bayesian theory of rational decision making, based on subjective calculations of expected utility, has faced powerful attack from philosophers such as David Lewis and Brian Skyrms, who advance an alternative causal decision theory. The test they present for the Bayesian is exemplified in the decision problem known as 'Newcomb's paradox' and in related decision problems and is held to support the prescriptions of the causal theory. As well as his conclusions, the concepts and methods of (...) Professor Eells introduces in the course of his analyses have extensive implications, not solely for probability theorists narrowly conceived, but for economists, statisticians and psychologists concerned with decision making and the employment of Bayesian principles. They and their students will, in addition, find the early chapters of great use as a background and introduction to the subject as a whole. (shrink)