This paper explores the prospects of employing a functional approach in order to improve our concept of actual causation. Claims of actual causation play an important role for a variety of purposes. In particular, they are relevant for identifying suitable targets for intervention, and they are relevant for our practices of ascribing responsibility. I argue that this gives rise to the _challenge of purpose_. The challenge of purpose arises when different goals demand adjustments of the concept that pull in opposing (...) directions. More specifically, I argue that a common distinction between certain kinds of preempted and preempting factors is difficult to motivate from an interventionist viewpoint. This indicates that an appropriately revised concept of actual causation would not distinguish between these two kinds of factors. From the viewpoint of retributivist responsibility, however, the distinction between preempted and preempting factors sometimes is important, which indicates that the distinction should be retained. (shrink)

Cancer biology features the ascription of normal functions to parts of cancers. At least some ascriptions of function in cancer biology track local normality of parts within the global abnormality of the aberration to which those parts belong. That is, cancer biologists identify as functions activities that, in some sense, parts of cancers are supposed to perform, despite cancers themselves having no purpose. The present paper provides a theory to accommodate these normal function ascriptions—I call it the Modeling Account of (...) Normal Function (MA). MA comprises two claims. First, normal functions are activities whose performance by the function-bearing part contributes to the self-maintenance of the whole system and, thereby, results in the continued presence of that part. Second, MA holds that models of system-level activities that are (partly) constitutive of self-maintenance are improved by including a representation of the relevant function-bearing part and by making reference to the activity/activities that part performs which contribute(s) to those system-level activities. I contrast MA with two other accounts that seek to explicate the ascription of normal functions in biology, namely, the organizational account and the selected effects account. Both struggle to extend to cancer biology. However, I offer ecumenical readings which allow them to recover some ascriptions of normal function to parts of cancers. So, though I contend that MA excels in this respect, the purpose of this paper is served if it provides materials for bridging the gap between cancer biology, philosophy of cancer, and the literature on function. (shrink)

When evaluating theories of causation, intuitions should not play a decisive role, not even intuitions in flawlessly-designed thought experiments. Indeed, no coherent theory of causation can respect the typical person’s intuitions in redundancy (pre-emption) thought experiments, without disrespecting their intuitions in threat-and-saviour (switching/short-circuit) thought experiments. I provide a deductively sound argument for these claims. Amazingly, this argument assumes absolutely nothing about the nature of causation. I also provide a second argument, whose conclusion is even stronger: the typical person’s causal intuitions (...) are thoroughly unreliable. This argument proceeds by raising the neglected question: in what respects is information about intermediate and enabling variables relevant to reliable causal judgment? (shrink)

Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence, a fast-growing research area that is so far lacking in firm theoretical foundations. In this article, an expanded version of a paper originally presented at the 37th Conference on Uncertainty in Artificial Intelligence, we attempt to fill this gap. Building on work in logic, probability, and causality, we establish the central role of (...) necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We propose a novel formulation of these concepts, and demonstrate its advantages over leading alternatives. We present a sound and complete algorithm for computing explanatory factors with respect to a given context and set of agentive preferences, allowing users to identify necessary and sufficient conditions for desired outcomes at minimal cost. Experiments on real and simulated data confirm our method’s competitive performance against state of the art XAI tools on a diverse array of tasks. (shrink)

A cause is proportional to its effect when, roughly speaking, it is at the right level of detail. There is a lively debate about whether proportionality is a necessary condition for causation. One of the main arguments against a proportionality constraint on causation is that many ordinary and seemingly perfectly acceptable causal claims cite causes that are not proportional to their effects. In this paper, I suggest that proponents of a proportionality constraint can respond to this objection by developing an (...) idea that is present in Yablo’s early work on proportionality, but which has strangely been ignored by both Yablo and others in the subsequent debate. My suggestion is that proportionality—and, indeed, causation itself—is relative to a domain of events. At the metaphysical level, this means that the causal relation has an extra relatum—namely, a domain of events. At the level of language, it introduces a new way in which causal claims are context-sensitive: what is expressed by a causal claim depends on the contextually relevant domain of events. As I argue, this suggestion allows us to accommodate the truth of ordinary causal claims while extending the explanatory benefits of a proportionality constraint. (shrink)

We argue that explainable artificial intelligence (XAI), specifically reason-giving XAI, often constitutes the most suitable way of ensuring that someone can properly be held responsible for decisions that are based on the outputs of artificial intelligent (AI) systems. We first show that, to close moral responsibility gaps (Matthias 2004), often a human in the loop is needed who is directly responsible for particular AI-supported decisions. Second, we appeal to the epistemic condition on moral responsibility to argue that, in order to (...) be responsible for her decision, the human in the loop has to have an explanation available of the system’s recommendation. Reason explanations are especially well-suited to this end and we examine whether – and how – it might be possible to make such explanations fit with AI systems. We support our claims by focusing on a case of disagreement between human in the loop and AI system. (shrink)

Recently, there have been several attempts to generalize the counterfactual theory of causal explanations to mathematical explanations. The central idea of these attempts is to use conditionals whose antecedents express a mathematical impossibility. Such countermathematical conditionals are plugged into the explanatory scheme of the counterfactual theory and—so is the hope—capture mathematical explanations. Here, I dash the hope that countermathematical explanations simply parallel counterfactual explanations. In particular, I show that explanations based on countermathematicals are susceptible to three problems counterfactual explanations do (...) not face. These problems seriously challenge the prospects for a counterfactual theory of explanation that is meant to cover mathematical explanations. (shrink)

In this dissertation I develop a pluralist theory of actual causation. I argue that we need to distinguish between total, path-changing, and contributing actual causation. The pluralist theory accounts for a set of example cases that have raised problems for extant unified theories and it is supported by considerations about the various functions of causal concepts. The dissertation also analyses the context-sensitivity of actual causation. I show that principled accounts of causal reasoning in legal inquiry face limitations and I argue (...) that the context-sensitivity of actual causation is best represented by a distinction between default and deviant states in causal models. (shrink)

Cognitive scientists have been debating how the folk concept of intentional action works. We suggest a simple account: people consider that an agent did X intentionally to the extent that X was causally dependent on how much the agent wanted X to happen (or not to happen). Combined with recent models of human causal cognition, this definition provides a good account of the way people use the concept of intentional action, and offers natural explanations for puzzling phenomena such as the (...) side-effect effect. We provide empirical support for our theory, in studies where we show that people’s causation and intentionality judgments track each other closely, in everyday situations as well as in scenarios with unusual causal structures. Study 5 additionally shows that the effect of norm violations on intentionality judgments depends on the causal structure of the situation, in a way uniquely predicted by our theory. Taken together, these results suggest that the folk concept of intentional action has been difficult to define because it is made of cognitive building blocks, such as our intuitive concept of causation, whose logic cognitive scientists are just starting to understand. (shrink)

Pearl opened the door to formally defining actual causation using causal models. His approach rests on two strategies: first, capturing the widespread intuition that X = x causes Y = y iff X = x is a Necessary Element of a Sufficient Set for Y = y, and second, showing that his definition gives intuitive answers on a wide set of problem cases. This inspired dozens of variations of his definition of actual causation, the most prominent of which are due (...) to Halpern & Pearl. Yet all of them ignore Pearl’s first strategy, and the second strategy taken by itself is unable to deliver a consensus. This paper offers a way out by going back to the first strategy: it offers six formal definitions of causal sufficiency and two interpretations of necessity. Combining the two gives twelve new definitions of actual causation. Several interesting results about these definitions and their relation to the various Halpern & Pearl definitions are presented. Afterwards the second strategy is evaluated as well. In order to maximize neutrality, the paper relies mostly on the examples and intuitions of Halpern & Pearl. One definition comes out as being superior to all others, and is therefore suggested as a new definition of actual causation. (shrink)

I argue that we need to distinguish between three concepts of actual causation: total, path-changing, and contributing actual causation. I provide two lines of argument in support of this account. First, I address three thought experiments that have been troublesome for unified accounts of actual causation, and I show that my account provides a better explanation of corresponding causal intuitions. Second, I provide a functional argument: if we assume that a key purpose of causal concepts is to guide agency, we (...) are better off making a distinction between three concepts of actual causation. (shrink)

This article presents a new argument for incorporating a distinction between default and deviant values into the formalism of causal models. The argument is based on considerations about how causal reasoners should represent disagreement over causes, and it is defended against an objection that has been raised against earlier arguments for defaults.

Goldman (1967) proposed that a subject s knows p if and only if p is appropriately causally connected to s’s believing p. He later on abandoned this theory (Goldman, 1976). The main objection to the theory is that the causal connection required by Goldman is compatible with certain problematic forms of luck. In this paper we argue that Goldman’s causal theory of knowledge can overcome the luck problem if causation is understood along interventionist lines. We also show that the modified (...) theory leads to the correct results in contexts involving other prominent forms of epistemic luck and compare it with other accounts on the market. (shrink)

How should we reason in science? Jan Sprenger and Stephan Hartmann offer a refreshing take on classical topics in philosophy of science, using a single key concept to explain and to elucidate manifold aspects of scientific reasoning. They present good arguments and good inferences as being characterized by their effect on our rational degrees of belief. Refuting the view that there is no place for subjective attitudes in 'objective science', Sprenger and Hartmann explain the value of convincing evidence in terms (...) of a cycle of variations on the theme of representing rational degrees of belief by means of subjective probabilities (and changing them by Bayesian conditionalization). In doing so, they integrate Bayesian inference—the leading theory of rationality in social science—with the practice of 21st century science. Bayesian Philosophy of Science thereby shows how modeling such attitudes improves our understanding of causes, explanations, confirming evidence, and scientific models in general. It combines a scientifically minded and mathematically sophisticated approach with conceptual analysis and attention to methodological problems of modern science, especially in statistical inference, and is therefore a valuable resource for philosophers and scientific practitioners. (shrink)

Many theories of actual causation implicitly endorse the claim that if c is an actual cause of e, then either c causes e directly or every intermediary by which c indirectly causes e is itself both an actual cause of e and also an actual effect of c. We think this compositionality constraint is plausible. However, as we show, it is not always satisfied by the causal attributions ordinary people make. We conclude by considering what philosophers working on causation should (...) do when the deliverances of their theories diverge from what ordinary people say. (shrink)

Roughly put, explanatory circles — if any exist — would be propositions such that (i) each explains the next, and (ii) the last explains the first. In this paper, I give two arguments for the view that there are explanatory circles. The first argument appeals to general relativistic worlds in which time is circular. The second argument appeals to special science theories that describe feedback loops. In addition, I show that three standard arguments against explanatory circles are unsuccessful.

In this paper I will argue that what makes our ordinary judgements about token causation true can be explicated in terms of interferences into quasi-inertial processes. These interferences and quasi-inertial processes can in turn be fully explicated in scientific terms. In this sense the account presented here is reductive. I will furthermore argue that this version of a process-theory of causation can deal with the traditional problems that process theories have to face, such as the problem of misconnection and the (...) problem of disconnection as well as with a problem concerning the mis-classification of pre-emption cases. (shrink)

Pre-emption cases have been taken by almost everyone to imply the unviability of the simple counterfactual theory of causation. Yet there is ample motivation from scientific practice to endorse a simple version of the theory if we can. There is a way in which a simple counterfactual theory, at least if understood contrastively, can be supported even while acknowledging that intuition goes firmly against it in pre-emption cases—or rather, only in some of those cases. For I present several new pre-emption (...) cases in which causal intuition does not go against the counterfactual theory, a fact that has been verified experimentally. I suggest an account of framing effects that can square the circle. Crucially, this account offers hope of theoretical salvation—but only to the counterfactual theory of causation, not to others. Again, there is (admittedly only preliminary) experimental support for this account. (shrink)

Theories of explanation need to account for a puzzling feature of our explanatory practices: the fact that we prefer explanations that are relatively abstract but only moderately so. Contra Franklin-Hall ([2016]), I argue that the interventionist account of explanation provides a natural and elegant explanation of this fact. By striking the right balance between specificity and generality, moderately abstract explanations optimally subserve what interventionists regard as the goal of explanation, namely identifying possible interventions that would have changed the explanandum.

Reductionists say things like: all mental properties are physical properties; all normative properties are natural properties. I argue that the only way to resist reductionism is to deny that causation is difference making (thus making the epistemology of causation a mystery) or to deny that properties are individuated by their causal powers (thus making properties a mystery). That is to say, unless one is happy to deny supervenience, or to trivialize the debate over reductionism. To show this, I argue that (...) if properties are individuated by their causal powers then, surprisingly, properties are individuated by necessary co-exemplification. (shrink)

The counterfactual tradition to defining actual causation has come a long way since Lewis started it off. However there are still important open problems that need to be solved. One of them is the (in)transitivity of causation. Endorsing transitivity was a major source of trouble for the approach taken by Lewis, which is why currently most approaches reject it. But transitivity has never lost its appeal, and there is a large literature devoted to understanding why this is so. Starting from (...) a survey of this work, we will develop a formal analysis of transitivity and the problems it poses for causation. This analysis provides us with a sufficient condition for causation to be transitive, a sufficient condition for dependence to be necessary for causation, and several characterisations of the transitivity of dependence. Finally, we show how this analysis leads naturally to several conditions a definition of causation should satisfy, and use those to suggest a new definition of causation. (shrink)

A series of recent studies have explored the impact of people's judgments regarding physical law, morality, and probability. Surprisingly, such studies indicate that these three apparently unrelated types of judgments often have precisely the same impact. We argue that these findings provide evidence for a more general hypothesis about the kind of cognition people use to think about possibilities. Specifically, we suggest that this aspect of people's cognition is best understood using an idea developed within work in the formal semantics (...) tradition, namely the notion of modality. On the view we propose, people may have separate representations for physical, moral and probabilistic considerations, but they also integrate these various considerations into a unified representation of modality. (shrink)

It is natural to think of causes as difference-makers. What exact difference causes make, however, is an open question. In this paper, I argue that the right way of understanding difference-making is in terms of causal processes: causes make a difference to a causal process that leads to the effect. I will show that this way of understanding difference-making nicely captures the distinction between causing an outcome and helping determine how the outcome happens and, thus, explains why causation is not (...) transitive. Moreover, the theory handles tricky cases that are problematic for competing accounts of difference-making. (shrink)

Existing research suggests that people's judgments of actual causation can be influenced by the degree to which they regard certain events as normal. We develop an explanation for this phenomenon that draws on standard tools from the literature on graphical causal models and, in particular, on the idea of probabilistic sampling. Using these tools, we propose a new measure of actual causal strength. This measure accurately captures three effects of normality on causal judgment that have been observed in existing studies. (...) More importantly, the measure predicts a new effect ("abnormal deflation"). Two studies show that people's judgments do, in fact, show this new effect. Taken together, the patterns of people's causal judgments thereby provide support for the proposed explanation. (shrink)

Imagine you and your friend Pierre agreed on meeting each other at a café, but he does not show up. What is the difference between a friend’s not showing up meeting? and any other person not coming? In some sense, all people who did not come show the same kind of behaviour, but most people would be willing to say that the absence of a friend who you expected to see is different in kind. In this paper, I will spell (...) out this difference by investigating laypeople’s conceptualisation of absences of actions in four experiments. In languages such as German, French, Italian, or Polish, people consider a friend’s not coming an omission. Any other person’s not coming, in contrast, is not considered an omission at all, but just a mere nothing. This use of the term omission differs from the usage in English, where ‘omission’ refers to all kinds of absences. In addition, ‘omission’ is not even an everyday term, but invented by philosophers for the sake of philosophical investigation. In other languages, ‘omission’ is part of an everyday vocabulary. Finally, I will discuss how this folk concept of omission could be made fruitful for philosophical questions. (shrink)

Philosophers discussing causation take on, as one of their responsibilities, the task of specifying an ontology of causation. Both standard and non-standard accounts of that ontology make two assumptions: that the ontological category of causal relata admits a unique specification, and that cause and effect are of the same ontological type. These assumptions are rarely made explicit, but there is in fact little reason to think them true. It is argued here that, if the question has any interest, there are (...) some considerations in favor of rejecting Uniqueness and Uniformity, no good reasons in favor of them, but good reason to think that we are not in a position to make a decisive pronouncement, since some of the information needed to make a judgement on the matter is empirical in character. Philosophers have therefore made two mistakes about the relata of causation: one is to have accepted unwarranted restrictions on the categories pertinent to causation; the other is to have wrongly assumed that we are in a position to name them. (shrink)

Structural models of systems of causal connections have become a common tool in the analysis of the concept of causation. In the present paper I offer a general argument to show that one of the most powerful definitions of the concept of actual cause, provided within the structural models framework, is not sufficient to grant a full account of our intuitive judgements about actual causation, so that we are still waiting for a comprehensive definition. This is done not simply by (...) focusing on a set of case studies, but by arguing that our intuitions about two different kinds of causal patterns, i.e., overdetermination and counterdetermination, cannot be addressed using that definition. (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.

We are responsible for some things but not for others. In this thesis, I investigate what it takes for an entity to be responsible for something. This question has two components: agents and actions. I argue for a permissive view about agents. Entities such as groups or artificially intelligent systems may be agents in the sense required for responsibility. With respect to actions, I argue for a causal view. The relation in virtue of which agents are responsible for actions is (...) a causal one. I claim that responsibility requires causation and I develop a causal account of agency. This account is particularly apt for addressing the relationship between agency and moral responsibility and sheds light on the causal foundations of moral responsibility. (shrink)

Hall [(2007), Philosophical Studies, 132, 109–136] offers a critique of structural equations accounts of actual causation, and then offers a new theory of his own. In this paper, I respond to Hall’s critique, and present some counterexamples to his new theory. These counterexamples are then diagnosed.

When agents violate norms, they are typically judged to be more of a cause of resulting outcomes. In this paper, we suggest that norm violations also affect the causality attributed to other agents, a phenomenon we refer to as "causal superseding." We propose and test a counterfactual reasoning model of this phenomenon in four experiments. Experiments 1 and 2 provide an initial demonstration of the causal superseding effect and distinguish it from previously studied effects. Experiment 3 shows that this causal (...) superseding effect is dependent on a particular event structure, following a prediction of our counterfactual model. Experiment 4 demonstrates that causal superseding can occur with violations of non-moral norms. We propose a model of the superseding effect based on the idea of counterfactual sufficiency. (shrink)

The literature on causation distinguishes between causal claims relating properties or types and causal claims relating individuals or tokens. Many authors maintain that corresponding to these two kinds of causal claims are two different kinds of causal relations. Whether to regard causal relations among variables as yet another variety of causation is also controversial. This essay maintains that causal relations obtain among tokens and that type causal claims are generalizations concerning causal relations among these tokens.

Past work has demonstrated that people’s moral judgments can influence their judgments in a number of domains that might seem to involve straightforward matters of fact, including judgments about freedom, causation, the doing/allowing distinction, and intentional action. The present studies explore whether the effect of morality in these four domains can be explained by changes in the relevance of alternative possibilities. More precisely, we propose that moral judgment influences the degree to which people regard certain alternative possibilities as relevant, which (...) in turn impacts intuitions about freedom, causation, doing/allowing, and intentional action. Employing the stimuli used in previous research, Studies 1a, 2a, 3a, and 4a show that the relevance of alternatives is influenced by moral judgments and mediates the impact of morality on non-moral judgments. Studies 1b, 2b, 3b, and 4b then provide direct empirical evidence for the link between the relevance of alternatives and judgments in these four domains by manipulating (rather than measuring) the relevance of alternative possibilities. Lastly, Study 5 demonstrates that the critical mechanism is not whether alternative possibilities are considered, but whether they are regarded as relevant. These studies support a unified framework for understanding the impact of morality across these very different kinds of judgments. (shrink)

When people want to identify the causes of an event, assign credit or blame, or learn from their mistakes, they often reflect on how things could have gone differently. In this kind of reasoning, one considers a counterfactual world in which some events are different from their real-world counterparts and considers what else would have changed. Researchers have recently proposed several probabilistic models that aim to capture how people do (or should) reason about counterfactuals. We present a new model and (...) show that it accounts better for human inferences than several alternative models. Our model builds on the work of Pearl (2000), and extends his approach in a way that accommodates backtracking inferences and that acknowledges the difference between counterfactual interventions and counterfactual observations. We present six new experiments and analyze data from four experiments carried out by Rips (2010), and the results suggest that the new model provides an accurate account of both mean human judgments and the judgments of individuals. (shrink)

While structural equations modeling is increasingly used in philosophical theorizing about causation, it remains unclear what it takes for a particular structural equations model to be correct. To the extent that this issue has been addressed, the consensus appears to be that it takes a certain family of causal counterfactuals being true. I argue that this account faces difficulties in securing the independent manipulability of the structural determination relations represented in a correct structural equations model. I then offer an alternate (...) understanding of structural determination, and I demonstrate that this theory guarantees that structural determination relations are independently manipulable. The account provides a straightforward way of understanding hypothetical interventions, as well as a criterion for distinguishing hypothetical changes in the values of variables which constitute interventions from those which do not. It additionally affords a semantics for causal counterfactual conditionals which is able to yield a clean solution to a problem case for the standard ‘closest possible world’ semantics. (shrink)

Grounding is often glossed as metaphysical causation, yet no current theory of grounding looks remotely like a plausible treatment of causation. I propose to take the analogy between grounding and causation seriously, by providing an account of grounding in the image of causation, on the template of structural equation models for causation.

This paper argues that so-called “trumping preemption” is in fact overdetermination or early preemption, and is thus not a distinctive form of redundant causation. I draw a novel lesson from cases thought to be trumping: that the boundary between preemption and overdetermination should be reconsidered.

Life-science phenomena are often explained by specifying the mechanisms that bring them about. The new mechanistic philosophers have done much to substantiate this claim and to provide us with a better understanding of what mechanisms are and how they explain. Although there is disagreement among current mechanists on various issues, they share a common core position and a seeming commitment to some form of scientific realism. But is such a commitment necessary? Is it the best way to go about mechanistic (...) explanation? In this article, we propose an alternative antirealist account that also fits explanatory practice in the life sciences. We pay special attention to mechanistic models, i.e. scientific models that involve a mechanism, and to the role of coherence considerations in building such models. To illustrate our points, we consider the mechanism for the action potential. 1 Introduction2 Some Core Features of Mechanistic Explanation3 Scientific Realism and Mechanistic Explanation4 Antirealist Mechanistic Explanation: The Case of the Action Potential5 Some Outstanding Issues for the Antirealist Mechanist6 Two Problems for the Realist Mechanist7 Conclusions. (shrink)

Consider the following causal exclusion principle: For all distinct properties F and F* such that F* supervenes on F, F and F* do not both cause a property G. Peter Menzies and Christian List have proven that it follows from a natural conception of causation as difference-making that this exclusion principle is not generally true. Rather, it turns out that whether the principle is true is a contingent matter. In addition, they have shown that in a wide range of empirically (...) ordinary cases, it follows that F* causes G and F does not. These cases plausibly include instances where F* is a mental property and F and G are physical properties. If this is the right conception of causation, it therefore turns out that the physical world is not causally closed. In this paper I show that there is an alternative conception of causation as difference-making that does not have the same consequences. Whether the physical world is causally closed therefore turns out to depend, inter alia, on which conception of difference-making is correct. I give a number of arguments for the alternative conception of difference-making. (shrink)

Judea Pearl won the 2010 Rumelhart Prize in computational cognitive science due to his seminal contributions to the development of Bayes nets and causal Bayes nets, frameworks that are central to multiple domains of the computational study of mind. At the heart of the causal Bayes nets formalism is the notion of a counterfactual, a representation of something false or nonexistent. Pearl refers to Bayes nets as oracles for intervention, and interventions can tell us what the effect of action will (...) be or what the effect of counterfactual possibilities would be. Counterfactuals turn out to be necessary to understand thought, perception, and language. This selection of papers tells us why, sometimes in ways that support the Bayes net framework and sometimes in ways that challenge it. (shrink)

Recent accounts of actual causation are stated in terms of extended causal models. These extended causal models contain two elements representing two seemingly distinct modalities. The first element are structural equations which represent the or mechanisms of the model, just as ordinary causal models do. The second element are ranking functions which represent normality or typicality. The aim of this paper is to show that these two modalities can be unified. I do so by formulating two constraints under which extended (...) causal models with their two modalities can be subsumed under so called which contain just one modality. These two constraints will be formally precise versions of Lewissystem of weights or priorities” governing overall similarity between possible worlds. (shrink)

An actual cause of some token effect is itself a token event that helped to bring about that effect. The notion of an actual cause is different from that of a potential cause – for example a pre-empted backup – which had the capacity to bring about the effect, but which wasn't in fact operative on the occasion in question. Sometimes actual causes are also distinguished from mere background conditions: as when we judge that the struck match was a cause (...) of the fire, while the presence of oxygen was merely part of the relevant background against which the struck match operated. Actual causation is also to be distinguished from type causation: actual causation holds between token events in a particular, concrete scenario; type causation, by contrast, holds between event kinds in scenario kinds. (shrink)

Counterfactual theories of causation have had difficulty in delivering the intuitively correct verdicts for cases of causation involving preemption, without generating further counterexamples. Hall has offered a pluralistic theory of causation, according to which there are two concepts of causation: counterfactual dependence and production. Hall’s theory does deliver the correct verdicts for many of the problematic kinds of preemption. It also deals successfully with cases of causation by omission, which have proved stubborn counterexamples to physical process theories of causation. Hall’s (...) theory therefore appears to be a significant improvement on extant univocal theories of causation, both physical and counterfactual. In this paper I present a series of counterexamples to Hall’s theory. I also describe cases in which our causal judgments appear to be sensitive to moral considerations. It does not seem likely that conventional theories of causation, which attempt to situate causation in an objective metaphysical picture of the world, will ever accord our intuitions in such cases. Finally, the notion of responsibility is considered, but rejected as an illuminating primitive for analyzing causation. (shrink)

In recent years, the debate on the problem of causal exclusion has seen an ‘interventionist turn’. Numerous non-reductive physicalists (e.g. Shapiro and Sober 2007) have argued that Woodward's (2003) interventionist theory of causation provides a means to empirically establish the existence of non-reducible mental-to-physical causation. By contrast, Baumgartner (2010) has presented an interventionist exclusion argument showing that interventionism is in fact incompatible with non-reductive physicalism. In response, a number of revised versions of interventionism have been suggested that are compatible with (...) non-reductive physicalism. The first part of this paper reconstructs the definitional details of these modified interventionist theories. The second part investigates whether the modification proposed in Woodward (2011) is not only compatible with, but moreover supports non-reductive physicalism. In particular, it is examined whether that newest variant of interventionism allows for empirically resolving the problem of causal exclusion as envisaged by Shapiro, Sober and others. (shrink)

Partial explanations are everywhere. That is, explanations citing causes that explain some but not all of an effect are ubiquitous across science, and these in turn rely on the notion of degree of explanation. I argue that current accounts are seriously deficient. In particular, they do not incorporate adequately the way in which a cause’s explanatory importance varies with choice of explanandum. Using influential recent contrastive theories, I develop quantitative definitions that remedy this lacuna, and relate it to existing measures (...) of degree of causation. Among other things, this reveals the precise role here of chance, as well as bearing on the relation between causal explanation and causation itself. (shrink)

Recent work on the interpretation of counterfactual conditionals has paid much attention to the role of causal independencies. One influential idea from the theory of Causal Bayesian Networks is that counterfactual assumptions are made by intervention on variables, leaving all of their causal non-descendants unaffected. But intervention is not applicable across the board. For instance, backtracking counterfactuals, which involve reasoning from effects to causes, cannot proceed by intervention in the strict sense, for otherwise they would be equivalent to their consequents. (...) We discuss these and similar cases, focusing on two factors which play a role in determining whether and which causal parents of the manipulated variable are affected: Speakers' need for an explanation of the hypothesized state of affairs, and differences in the ‘resilience’ of beliefs that are independent of degrees of certainty. We describe the relevant theoretical notions in some detail and provide experimental evidence that these factors do indeed affect speakers' interpretation of counterfactuals. (shrink)