The explanatory role of natural selection is one of the long-term debates in evolutionary biology. Nevertheless, the consensus has been slippery because conceptual confusions and the absence of a unified, formal causal model that integrates different explanatory scopes of natural selection. In this study we attempt to examine two questions: (i) What can the theory of natural selection explain? and (ii) Is there a causal or explanatory model that integrates all natural selection explananda? For the first question, we argue that (...) five explananda have been assigned to the theory of natural selection and that four of them may be actually considered explananda of natural selection. For the second question, we claim that a probabilistic conception of causality and the statistical relevance concept of explanation are both good models for understanding the explanatory role of natural selection. We review the biological and philosophical disputes about the explanatory role of natural selection and formalize some explananda in probabilistic terms using classical results from population genetics. Most of these explananda have been discussed in philosophical terms but some of them have been mixed up and confused. We analyze and set the limits of these problems. (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.

This paper examines a promising probabilistic theory of singular causation developed by David Lewis. I argue that Lewis' theory must be made more sophisticated to deal with certain counterexamples involving pre-emption. These counterexamples appear to show that in the usual case singular causation requires an unbroken causal process to link cause with effect. I propose a new probabilistic account of singular causation, within the framework developed by Lewis, which captures this intuition.

This article introduces the notion of the directedness of a process, which underlies event causation as well as the persistence of things. Using this notion it investigates what happens in typical cases of active event causation. Causes never necessitate their effects because even non- probabilistic causes can be counteracted.

Counter factual theories of Causation have had problems with cases of probabilistic causation and preemption. I put forward a counterfactual theory that seems to deal with these problematic cases and also has the virtue of providing an account of the alleged asymmetry between hasteners and delayers: the former usually being counted as causes, the latter not. I go on to consider a new type of problem case that has not received so much attention in the literature, those (...) I dub catalysts and anti-catalysts, and show how my account needs to be adjusted to deliver the right verdicts in these cases. The net result is a particular conception of a cause that I try to spell out in the closing section of the paper. In that section, I also briefly discuss causal asymmetry and the purpose behind providing a counterfactual theory of causation. (shrink)

In a recent article in this journal, Federica Russo and Jon Williamson argue that an analysis of causality in terms of probabilistic relationships does not do justice to the use of mechanistic evidence to support causal claims. I will present Ronald Giere's theory of probabilistic causation, and show that it can account for the use of mechanistic evidence (both in the health sciences—on which Russo and Williamson focus—and elsewhere). I also review some other probabilistic theories of (...) causation (of Suppes, Eells, and Humphreys) and show that they cannot account for the use of mechanistic evidence. I argue that these theories are also inferior to Giere's theory in other respects. (shrink)

In this paper, it is shown that an event E can be the cause of an event E* even if there is a less than 100% likelihood that, given an arbitrary E-similar event, an E*-similar event will ensue.

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)

A probabilistic and counterfactual theory of causality is developed within the framework of branching time. The theory combines ideas developed by James Fetzer, Donald Nute, Patrick Suppes, Ming Xu, John Pollock, David Lewis and Mellor among others.

In this paper I argue that economic theories have never been able to provide a coherent explanation of the causation requirement in tort law. The economic characterization of this requirement faces insurmountable difficulties, because discourse on tort liability cannot be reduced to a cost-benefit analysis without a loss of meaning. More seriously, I try to show that by describing causation in economic terms, economic theories offer an image of the practice in which the participants incur in logical contradictions (...) and develop patterns of inference that are far from intuitive. For this reason, efficiency cannot be the fundamental principle underlying tort law. Finally, I suggest that economic analysis of law can provide a genuine explanation of certain aspects of legal practice if it relinquishes its reductionist claims. (shrink)

The investigation of probabilistic causality has been plagued by a variety of misconceptions and misunderstandings. One has been the thought that the aim of the probabilistic account of causality is the reduction of causal claims to probabilistic claims. Nancy Cartwright (1979) has clearly rebutted that idea. Another ill-conceived idea continues to haunt the debate, namely the idea that contextual unanimity can do the work of objective homogeneity. It cannot. We argue that only objective homogeneity in combination with (...) a causal interpretation of Bayesian networks can provide the desired criterion of probabilistic causality. (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.

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 and Duncan can provide, when an additional assumption is added, adequate qualitative measures of one variableś 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)

In a recent article, Paul Noordhof (1999) has put forward an intriguing account of causation intended to work under the assumption of indeterminism. I am going to present four problems for the account, three which challenge the necessity of the conditions he specifies, and one which challenges their joint-sufficiency.

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 variableś 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)

Hilary Bowman-Smart et al. (2023) have rightly pointed out that one of the recurring criticisms of the use of noninvasive prenatal testing (NIPT) for non-medical trait prediction is the probabilist...

It seems to be generally accepted that (a) counterfactual conditionals are to be analysed in terms of possible worlds and inter-world relations of similarity and (b) causation is conceptually prior to counterfactuals. I argue here that both (a) and (b) are false. The argument against (a) is not a general metaphysical or epistemological one but simply that, structurally speaking, possible worlds theories are wrong: this is revealed when we try to extend them to cover the case of probabilistic (...) counterfactuals. Indeed a type of counterfactual probability exists which cannot be expressed in possible worlds terms at all. The argument against (b) emerges when we look at the form of an adequate account of both probabilistic and non-probabilistic counterfactuals. I do this by sketching and defending an approach to counterfactuals that, first, invoke a generalized notion of cause as primitive and, secondly, is algorithmic in form: counterfactuals are evaluated algorithmically in terms of other counterfactuals, without vicious circularity. Structures like possible worlds do not play a role either in general truth-conditions or in evaluation. They are simply the wrong sorts of structures. (shrink)

Actual causes - e.g. Suzy's being exposed to asbestos - often bring about their effects - e.g. Suzy's suffering mesothelioma - probabilistically. I use probabilistic causal models to tackle one of the thornier difficulties for traditional accounts of probabilistic actual causation: namely probabilistic preemption.

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)

Mental causation is notoriously threatened by the causal exclusion argument. A prominent strategy to save mental causation from causal exclusion consists in subscribing to an interventionist account of causation. This move has, however, recently been challenged by several authors. In this paper, we do two things: We (i) develop what we consider to be the strongest version of the interventionist causal exclusion argument currently on the market and (ii) propose a new way how it can in principle (...) be overcome. In particular, we propose to replace strict supervenience in the assumption that the mental supervenes on the physical by probabilistic supervenience and show how this move has the potential to license the inference to mental causation. Finally, we argue that probabilistic supervenience captures some of the most important intuitions that strict supervenience captures and discuss possible objections to weakening strict supervenience in the way we suggest. (shrink)

We present a probabilistic extension to active path analyses of token causation (Halpern & Pearl 2001, forthcoming; Hitchcock 2001). The extension uses the generalized notion of intervention presented in (Korb et al. 2004): we allow an intervention to set any probability distribution over the intervention variables, not just a single value. The resulting account can handle a wide range of examples. We do not claim the account is complete --- only that it fills an obvious gap in previous (...) active-path approaches. It still succumbs to recent counterexamples by Hiddleston (2005), because it does not explicitly consider causal processes. We claim three benefits: a detailed comparison of three active-path approaches, a probabilistic extension for each, and an algorithmic formulation. (shrink)

Larry Wright and others have advanced causal accounts of functional explanation, designed to alleviate fears about the legitimacy of such explanations. These analyses take functional explanations to describe second order causal relations. These second order relations are conceptually puzzling. I present an account of second order causation from within the framework of Eells' probabilistic theory of causation; the account makes use of the population-relativity of causation that is built into this theory.

We present a probabilistic extension to active path analyses of token causation. The extension uses the generalized notion of intervention presented in : we allow an intervention to set any probability distribution over the intervention variables, not just a single value. The resulting account can handle a wide range of examples. We do not claim the account is complete --- only that it fills an obvious gap in previous active-path approaches. It still succumbs to recent counterexamples by Hiddleston, (...) because it does not explicitly consider causal processes. We claim three benefits: a detailed comparison of three active-path approaches, a probabilistic extension for each, and an algorithmic formulation. (shrink)

A probabilistic theory of causation is a theory which holds that the central feature of causation is that causes raise the probability of their effects. In this dissertation, I defend Hans Reichenbach's original version of the probabilistic theory of causation, which analyses causal relations in terms of a three place statistical betweenness relation. Unlike most discussions of this theory, I hold that the statistical relation should be taken as a sufficient, but not as a necessary (...) , condition for causal betweenness. With this difference in interpretation, Reichenbach's theory is shown to be immune to all of the criticisms which have been raised against it in the last fifty years. ;Reichenbach's main purpose in defending a probabilistic theory of causation was to use it in giving an analysis of the direction of time. I defend this view, arguing that the arrow of causation is more basic than any of the other arrows of time---metaphysical, psychological or physical. In particular, I argue against the popular view that the direction of time is given by the physical fact that entropy only increases in one direction. I further show that the probabilistic theory of causation is particularly suited to the task of analysing the various arrows of time. In my final chapter, I also suggest that the probabilistic theory of causation may be fruitfully applied to the analysis of some important puzzles in the interpretation of quantum mechanics. (shrink)

It is argued in this paper that although much attention has been paid to causal chains and common causes within the literature on probabilistic causality, a primary virtue of that approach is its ability to deal with cases of multiple causation. In doing so some ways are indicated in which contemporary sine qua non analyses of causation are too narrow (and ways in which probabilistic causality is not) and an argument by Reichenbach designed to provide a (...) basis for the asymmetry of causation is refined. The importance of referring causal claims to an abstract model is also emphasized. (shrink)

This paper makes the case for a wider acceptance of a probabilistic approach to causation in negligence. This acceptance would help to remove much of the incoherence which has come to afflict the English law of personal injury law. This incoherence can also be found in other common law jurisdictions (notably those of the United States, Canada and Australia). Concentrating upon recent UK case law, the argument opposes the contention that ‘naked statistics’ can play no role in establishing (...) causation. The argument is controversial but it can be reduced to three unremarkable grounds: (1) With its acceptance (albeit in certain carefully prescribed circumstances) of liability for a negligently increased risk which has eventuated, the common law has already embraced a probabilistic conception of causation; (2) The English common law already employs a probabilistic (frequentist) approach to identifying coincidences; and (3) With the ‘balance of probabilities’ as the standard of proof in civil cases, the common law has long had a probabilistic (epistemic) concept at its core. Probabilistic approaches (at both the type and token level) are shown to be consistent with laypersons’ understanding of the concepts such as risk, chance, odds and likelihood. Moreover, a wider acceptance of a probabilistic perspective on causation would entail no major challenge to the fundamental aims of tort, viz. deterrence and corrective justice. (shrink)

Butterfield's (1992a,b,c) claim of the equivalence of absence of Lewisian probabilistic counterfactual causality (LC) to Hellman's stochastic Einstein locality (SEL) is questioned. Butterfield's assumption on which the proof of his claim is based would suffice to prove that SEL implies absence of LC also for appropriately given versions of these notions in algebraic quantum field theory, but the assumption is not an admissible one. The conclusion must be that the relation of SEL and absence of LC is open, and (...) that they may be independent. (shrink)

It is often argued that if a mentalproperty supervenes on a physical property, then (1)the mental property M ``inherits'''' its causal efficacyfrom the physical property P and (2) the causalefficacy of M reduces to that of P. However, once weunderstand the supervenience thesis and the concept ofcausation probabilistically, it turns out that we caninfer the causal efficacy of M from that of P andvice versa if and only if a certain condition, whichI call the ``line-up'''' thesis, holds. I argue that (...) thesupervenience thesis entails neither this conditionnor its denial. I also argue that even when theline-up thesis holds true, reductionism about thecausal efficacy of the mental property doesn''tfollow. (shrink)

The same two kinds of conditional connections in the world, each dependent on the situation, hold between each event in certain sets of events that we can call causal circumstances for the lighting. A causal circumstance cc) included the event that for some reason we pick out and call the cause -- the striking s).

In their article 'Causes and Explanations: A Structural-Model Approach. Part I: Causes', Joseph Halpern and Judea Pearl draw upon structural equation models to develop an attractive analysis of 'actual cause'. Their analysis is designed for the case of deterministic causation. I show that their account can be naturally extended to provide an elegant treatment of probabilistic causation.

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)

According to Nancy Cartwright, a causal law holds just when a certain probabilistic condition obtains in all test situations which in turn satisfy a set of background conditions. These background conditions are shown to be inconsistent and, on separate account, logically incoherent. I offer a corrective reformulation which also incorporates a strategy for problems like Hesslow's thrombosis case. I also show that Cartwright's recent argument for modifying the condition to appeal to singular causes fails.Proposed modifications of the theory's (...) class='Hi'>probabilistic condition to handle effects with extreme probabilities (0 or 1) are found unsatisfactory. I propose a unified solution which also handles extreme causes. Undefined conditional probabilities give rise to three good, but non-equivalent, ways of formulating the theory. Various formulations appear in the literature. I give arguments to eliminate all but one candidate. Finally, I argue for a crucial new condition clause, and show how to extend the results beyond a simple probabilistic framework. (shrink)

In this paper, I argue that causal theories of memory are typically committed to two independent, non-mutually entailing theses. The first thesis pertains to the necessity of appropriate causation in memory, specifying a condition token memories need to satisfy. The second pertains to the explanation of memory reliability in causal terms and it concerns memory as a type of mental state. Post-causal theories of memory can reject only the first (weak post-causalism) or both (strong post-causalism) theses. Upon this backdrop, (...) I examine Werning’s (2020) causalist argument from probabilistic correlation. I argue that it doesn’t establish the necessity of appropriate causation and thus it can only target strong post-causalist theories. I end up by presenting some general considerations, suggesting that memories may not always be causally linked to past experiences. (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)

After clarifying the probabilistic conception of causality suggested by Good (1961-2), Suppes (1970), Cartwright (1979), and Skyrms (1980), we prove a sufficient condition for transitivity of causal chains. The bearing of these considerations on the units of selection problem in evolutionary theory and on the Newcomb paradox in decision theory is then discussed.

Hume thought that if you believed in powers, you believed in necessary connections in nature. He was then able to argue that there were none such because anything could follow anything else. But Hume wrong-footed his opponents. A power does not necessitate its manifestations: rather, it disposes towards them in a way that is less than necessary but more than purely contingent. -/- In this paper a dispositional theory of causation is offered. Causes dispose towards their effects and often (...) produce them. But a set of causes, even though they may succeed in producing an effect, cannot necessitate it since the effect could have been counteracted by some additional power. This would require a separation of our concepts of causal production and causal necessitation. The most conspicuous cases of causation are those where powers accumulate and pass a requisite threshold for an effect to occur. -/- We develop a model for representing powers as constituent vectors within an n-dimensional quality space, where composition of causes appears as vector addition. Even our resultant vector, however, has to be understood as having dispositional force only. This model throws new light on causal modality and cases of prevention, causation by absence and probabilistic causation. (shrink)

The pairing argument aims to demonstrate the impossibility of non-spatial objects (including minds) standing in causal relations. Its chief premises are (roughly) that causation requires pairing relations between causes and effects and that pairing relations require spatial relations. Critics have argued that the first claim suffers from counterexamples involving indeterministic causation. After briefly rehearsing the pairing argument and the objection from indeterministic causation, I offer two ways of revising the pairing argument to meet the objection from indeterministic (...) causation. (shrink)