Probabilistic Causation

Scepticism concerning the idea of causation being linked to contingent chance-raising is articulated in Beebee’s challenging chapter. She suggests that none of these approaches will avoid the consequence that spraying defoliant on a weed is a cause of the weed’s subsequent health. We will always be able to abstract away enough of the healthy plant processes so all that’s left is the causal chain involving defoliation and health. In those circumstances, there will be contingent chance-raising. Beebee’s conclusion is that we (...) should reject the idea of contingent chance-raising and just accept that all causation involves chance-raising. This involves the reclassification of some intuitive cases of causation as causal processes without causation but rather hindering (a distinctive kind of process). It seems clear from this discussion and from the brief earlier remarks about the status of prevention that the classification of types of causal processes and the characterization of their link to causation are matters of some importance. (shrink)

We assess Cartwright's models for probabilistic causality, and in particular, her models for EPR-like experiments of quantum mechanics. We show that her models for the EPR are mathematically incorrect and physically implausible. Finally, we argue that her models are not adequate for EPR-phenomena, since they ignore modal and spatiotemporal aspects inherent in their setup.

Schaffer proposes a new account of probabilistic causation that synthesizes the probability-raising and process-linkage views on causation. The driving idea of Schaffer's account is that, although an effect does not invariably depend on its cause, a process linked to the effect does. In this paper, however, I will advance counterexamples to Schaffer's account and then demonstrate that Schaffer's possible responses to them do not work. Finally, I will argue that my counterexamples suggest that the driving idea of Schaffer's account is (...) misdirected. (shrink)

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 ...

In this paper I reconsider a standard counterexample to the chance-raising theory of singular causation. Extant versions of this theory are so different that it is difficult to formulate the core thesis that they all share, despite the guiding idea that causes raise the chance of their effects. At one extreme, ‘Humean’ theories – which can be traced to Reichenbach – say that a particular event of type C is the cause of a particular event of type E only if (...) P(E|C & K) > P(E|~C & K) where K is a set of background conditions and where the probabilities are interpreted as relative frequencies. At the other extreme, explicitly non-Humean theories take chance to be a physical, particular, local feature of the world. (shrink)

In this paper I offer an 'integrating account' of singular causation, where the term 'integrating' refers to the following program for analysing causation. There are two intuitions about causation, both of which face serious counterexamples when used as the basis for an analysis of causation. The 'process' intuition, which says that causes and effects are linked by concrete processes, runs into trouble with cases of 'misconnections', where an event which serves to prevent another fails to do so on a particular (...) occasion and yet the two events are linked by causal processes. The chance raising intuition, according to which causes raise the chance of their effects, easily accounts for misconnections but faces the problem of chance lowering causes, a problem easily accounted for by the process approach. The integrating program attempts to provide an analysis of singular causation by synthesising the two insights, so as to solve both problems. In this paper I show that extant versions of the integrating program due to Eells, Lewis, and Menzies fail to account for the chance-lowering counterexample. I offer a new diagnosis of the chance lowering case, and use that as a basis for an integrating account of causation which does solve both cases. In doing so, I accept various assumptions of the integrating program, in particular that there are no other problems with these two approaches. As an example of the process account, I focus on the recent CQ theory of Wesley Salmon (1997). (shrink)

Philosophers have long been fascinated by the connection between cause and effect: are 'causes' things we can experience, or are they concepts provided by our minds? The study of causation goes back to Aristotle, but resurged with David Hume and Immanuel Kant, and is now one of the most important topics in metaphysics. Most of the recent work done in this area has attempted to place causation in a deterministic, scientific, worldview. But what about the unpredictable and chancey world we (...) actually live in: can one theory of causation cover all instances of cause and effect? _Cause and Chance: Causation in an Indeterministic World _is a collection of specially written papers by world-class metaphysicians. Its focus is the problem facing the 'reductionist' approach to causation: the attempt to cover all types of causation, deterministic and indeterministic, with one basic theory. Contributors: Stephen Barker, Helen Beebee, Phil Dowe, Dorothy Edgington, Doug Ehring, Chris Hitchcock, Igal Kwart, Paul Noordhof, Murali Ramachandran and Michael Tooley. (shrink)

The present paper deals with the tools that can be used to represent causation and to reason about it and, specifically, with their diversity. It focuses on so-called “causal probabilities”—that is, probabilities of effects given one of their causes—and critically surveys a recent paper in which Joyce argues that the values of these probabilities do not depend on one’s conception of causation. I first establish a stronger independence claim: I show that the very definition of causal probabilities is independent of (...) one’s conception of causation. Second, I investigate whether causal probabilities indeed take the same values under their different possible definitions. (shrink)

Mellor's subject is singular causation between facts, expressed ‘E because C’. His central requirement for causation is that the chance that E if C be greater than the chance that E if C: chc(E)>chc(E). The book is as much about chance as it is about causation. I show that his way of distinguishing chc (E) from the traditional notion of conditional chance leaves than him with a problem about the existence of chQ(P) when Q is false (Section 3); and also (...) that any notion of chance which conforms to the standard calculus has wider application than the causal instances to which Mellor's notion is restricted (Section 8). Other topics discussed may be gleaned from the headings below. 1 Review of D.H. Mellor [1995]: The Facss of Causation, London, Routledge, International Library of Philosophy. (shrink)

In this important book, Ellery Eells explores and refines philosophical conceptions of probabilistic causality. In a probabilistic theory of causation, causes increase the probability of their effects rather than necessitate their effects in the ways traditional deterministic theories have specified. Philosophical interest in this subject arises from attempts to understand population sciences as well as indeterminism in physics. Taking into account issues involving spurious correlation, probabilistic causal interaction, disjunctive causal factors, and temporal ideas, Professor Eells advances the analysis of what (...) it is for one factor to be a positive causal factor for another. A salient feature of the book is a theory of token level probabilistic causation in which the evolution of the probability of a later event from an earlier event is central. This will be a book of crucial significance to philosophers of science and metaphysicians; it will also prove stimulating to many economists, psychologists, and physicists. (shrink)

During the past decades several philosophers of science and social scientists have been interested in the problems of causation. Recently attention has been given to probabilistic causation in dichotomous causal systems. The paper uses the basic features of probabilistic causation to argue that the causal modeling approaches developed by such researchers as Blalock (1964) and Duncan (1975) can provide, when an additional assumption is added, adequate qualitative measures of one variable causal influence upon another. Finally, some of the difficulties and (...) issues involved in developing adequate quantitative measures are discussed, and it is concluded that the causal modeling measures cannot provide adequate quantitative measures. (shrink)

The purpose of this paper is to provide a systematic defense of the single-case propensity account of probabilistic explanation from the criticisms advanced by Hanna and by Humphreys and to offer a critical appraisal of the aleatory conception advanced by Humphreys and of the deductive-nomological-probabilistic approach Railton has proposed. The principal conclusion supported by this analysis is that the Requirements of Maximal Specificity and of Strict Maximal Specificity afford the foundation for completely objective explanations of probabilistic explananda, so long as (...) they are employed on the basis of propensity criteria of explanatory relevance. (shrink)

In this paper I present both a critical appraisal of Humphreys' probabilistic theory of causality and a sketch of an alternative view of the relationship between the notions of probability and of cause. Though I do not doubt that determinism is false, I claim that the examples used to motivate Humphreys' theory typically refer to subjective rather than objective chance. Additionally, I argue on a number of grounds that Humphreys' suggestion that linear regression models be used as a canonical form (...) for the description of causal relations is untenable. I conclude by exploring the variety of ways in which probabilistic elements can be embedded into the structure of causal mechanisms. This investigation suggests both that deterministic mechanisms can produce stochastic behavior and stochastic mechanisms can produce deterministic behavior. Note: Link is to the article in a subscription database available to users affiliated with Butler University. Appropriate login information will be required for access. Users not affiliated with Butler University should contact their local librarian for assistance in locating a copy of this article. (shrink)

I argue that the orthodox account of probabilistic causation, on which probabilistic causes determine the probability of their effects, is inconsistent with certain ontological assumptions implicit in scientific practice. In particular, scientists recognize the possibility that properties of populations can cause the behavior of members of the populations. Such emergent population‐level causation is metaphysically impossible on the orthodoxy.

The starting point in the development of probabilistic analyses of token causation has usually been the naïve intuition that, in some relevant sense, a cause raises the probability of its effect. But there are well-known examples both of non-probability-raising causation and of probability-raising non-causation. Sophisticated extant probabilistic analyses treat many such cases correctly, but only at the cost of excluding the possibilities of direct non-probability-raising causation, failures of causal transitivity, action-at-a-distance, prevention, and causation by absence and omission. I show that (...) an examination of the structure of these problem cases suggests a different treatment, one which avoids the costs of extant probabilistic analyses. (shrink)

This paper argues that if the world is irreducibly stochastic, then both Salmon's S-R model of explanation and Fetzer's C-R model of explanation have the following undesirable consequence: the objective probability (associated with the model's relevance condition) of any actual macro-event is either undefined or else, if defined, it equals one--so that the event is not even a candidate for a probabilistic explanation. This result follows from the temporal ambiguity of ontic probability in an irreducibly stochastic world. It is argued (...) further that an analogous difficulty faces those theories of probabilistic causality which depend upon the notions of contributing and counteracting causes. Because of the problem of temporal ambiguity, it is not possible to objectively label a particular event as a contributing (or a counteracting) cause of some subsequent event. The argument is carried through in detail for a recent theory of probabilistic causality proposed by Paul Humphreys. (shrink)

“Probabilistic Causation” designates a group of theories that aim to characterize the relationship between cause and effect using the tools of probability theory. The central idea behind these theories is that causes change the probabilities of their effects. This article traces developments in probabilistic causation, including recent developments in causal modeling. A variety of issues within, and objections to, probabilistic theories of causation will also be discussed.

Causes often influence their effects via multiple routes. Moderate alcohol consumption can raise the level of HDL ('good') cholesterol, which in tum reduces the risk of heart disease. Unfortunately, moderate alcohol consumption can also increase the level of homocysteine, which in tum increases the risk of heart disease. The net or overall effect of alcohol consumption on heart disease will depend upon both of these routes, and no doubt upon many others as well. This is a familiar fact of life (...) for engineers and policy makers, one that often gives rise to unintended consequences. Suppose, for example, that the American Federal Aviation Administration were to institute new regulations requiring that aeroplanes be equipped with some expensive new safety feature. Would this regulation save lives? Not necessarily. Every dollar (or pound, or euro, or yen ... ) that an airline spends to upgrade its fleet is a dollar that must be recouped in some way, most likely through higher fares. Higher fares may, in tum, persuade some travellers to drive instead of fly- especially on shorter routes. But it is inherently more dangerous to drive a given route than to fly it, so the net effect of the new regulation may cost lives, rather than saving them (Glassner 1999: 188). (shrink)

Salmon has argued that science provides explanations by describing a causal nexus: For Salmon, this nexus is a network of processes and interactions. I argue that this picture of the causal nexus is insufficient for an account of scientific explanation: a taxonomy of causal relevance is also needed. ;Probabilistic theories of causation seem to provide such a taxonomy in their dichotomy between promoting and inhibiting causes. However, standard probabilistic theories are beset by a difficulty called the problem of disjunctive factors. (...) According to such theories, an effect is more probable in the presence of a cause than in its absence . But there are many ways in which a particular cause may come about, and many in which it may be absent; these ways need not confer the same probabilities upon the effect. Thus, there is no direct comparison of two probabilities to be made. My solution is to abandon the oversimplified dichotomy between promoting and inhibiting causes. Causal claims convey qualitative information about more complex probabilistic relationships. Interestingly, this picture of causation meshes naturally with extant theories of explanation. ;I show how this framework may be employed in resolving several outstanding problems in the theories of causation and explanation, such as the relationship between singular and general causal claims, and the role of contrastive stress in causal and explanatory claims. ;Finally, I show how my account of causal explanation can be connected with the semantic conception of scientific theories. According to this conception, a theory is characterized in terms of a class of mathematical models. I describe the relationship between these mathematical models and the probabilistic relationships that are described by causal explanations. These ideas are illustrated in an extended discussion of causation and explanation in evolutionary theory. (shrink)

Wesley Salmon provided three classic criteria of adequacy for satisfactory interpretations of probability. A fourth criterion is suggested here. A distinction is drawn between frequency‐driven probability models and theory‐driven probability models and it is argued that single case accounts of chance are superior to frequency accounts at least for the latter. Finally it is suggested that theories of chance should be required only to be contingently true, a position which is a natural extension of Salmon's ontic account of probabilistic causality (...) and his own later views on propensities. (shrink)

I argue that Nancy Cartwright's largely methodological arguments for capacities and against Hume's regularity account of causation are only partially successful. They are especially problematic in establishing the primacy of singular causation and the reality of mixed-dual capacities. Therefore, her arguments need to be supported by ontological ones, and I propose the propensity interpretation of causal probabilities as a natural way of doing this.

Are there ‘degrees of causation’? Yes and no: causation is not a scalar relation, but different causes can contribute to a causing of an effect to different extents. In this paper, I motivate a probabilistic analysis of an event’s degree of contribution to a causing of an effect and explore some of its consequences.

Many theorists take causality to be the heart of scientific explanation. If we couple this view with the idea, again common enough, that causes are something like sufficient conditions for their effects, then we have raised a difficulty for any theory of scientific explanation that admits "final" statistical explanations, for a statistical explanation must allow that factors relevant to an event to be explained not determine that event, but only make it probable to some degree . If we do not (...) wish to give up the possibility of genuine statistical explanations we must either give up this central causal intuition in explanation, or admit that causes act as something other than sufficient conditions--say in a probabilistic manner. This is, of course, little help unless we can clearly explicate the notion of a probabilistic cause, one of the tasks I have attempted to accomplish in this thesis. ;The general structure of this effort is in three parts. In the first chapter I generate the basic concepts underlying the notion of a probabilistic cause. In this I follow closely the development of this idea as constructed by Wesley Salmon. Within this view scientific explanation consists in the tracing of causal influence via causes as processes, and providing the probability relations among events which are the product of causal interactions. In the second chapter I further develop the idea of causal processes by comparing this notion with the more traditional analyses of causation, the regularity and counterfactual theories. There I show how the process ontology very naturally overcomes problems in both of these views. I also note the advantages this conception of causes holds over these received views with respect to the problem of determinism. In the third and final chapter I discuss, and attempt to overcome, difficulties that are specific to the notion of a probabilistic causal process. (shrink)

My dissertation defends the theory of probabilistic causation called "the unanimity theory" against three challenges, which I call "the metaphysical challenge", "the conceptual challenge" and "the methodological challenge". I argue that the unanimity theory meets these three challenges, and coheres with each of the three theories of probabilistic causation that have been naturally inspired by the three challenges. I conclude that the unanimity theory constitutes a foundation of the three theories, which in fact reveals the versatility of the unanimity theory. (...) ;In Chapter 1, I introduce the conceptual foundations of the theory of type-level probabilistic causation called "the unanimity theory". I critically examine John Dupre's theory and his objections to the unanimity theory. ;In Chapter 2, I discuss the "metaphysical challenge" to the unanimity theory. According to Nancy Cartwright, the unanimity theory does not fully reveal the "capacities" that causal laws assume. I argue that the unanimity theory in fact reveals these capacities. ;In Chapter 3, I discuss the "conceptual challenge" to the unanimity theory. According to Christopher Hitchcock, the comparison of the probability of a factor Y in the presence of a factor X with the probability of Y in the absence of X does not provide us with appropriate information about causal relevance. Hitchcock proposes a different theory of probabilistic causation called "the ternary theory." I argue that the unanimity theory meets the problems Hitchcock poses for it. I argue that the unanimity theory explicates the same causal relevance relations that the ternary theory is intended to explicate. ;In Chapter 4, I discuss the "methodological challenge" to the unanimity theory. According to Daniel Hausman, the unanimity theory cannot guide an agent's decision in two kinds of epistemic circumstances. The challenge requires us to consider a new theory called "the theory of irregular causation". I elucidate the pragmatic significance of the theory of irregular causation and the relation between the two theories, and conclude that the unanimity theory constitutes a foundation of the theory of irregular causation. (shrink)

Nancy Cartwright believes that we live in a Dappled World– a world in which theories, principles, and methods applicable in one domain may be inapplicable in others; in which there are no universal principles. One of the targets of Cartwright’s arguments for this conclusion is the Causal Markov condition, a condition which has been proposed as a universal condition on causal structures.1 The Causal Markov condition, Cartwright argues, is applicable only in a limited domain of special cases, and thus cannot (...) be used as a universal principle in causal discovery. I have no dispute with any of these claims here. Rather, I wish to argue for a very limited thesis: that the Causal Markov condition is applicable in the specific domain of microscopic quantum mechanical systems; further, that the condition can fruitfully be applied to the much discussed EPR setup. This is perhaps a surprising conclusion, for it is precisely in this domain that Cartwright’s arguments against the Causal Markov condition have been considered to be the most successful. (shrink)