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.

Cognitive Science, Volume 46, Issue 5, May 2022.

There have been several recent attempts to model ordinary intuitions about actual causation by combining a counterfactual definition of the causal relation with an abnormality-based account of causal judgments. In these models, the underlying psychological theory is that people automatically focus on abnormal events when judging the actual causes of an effect. This approach has enabled authors such as Halpern and Hitchcock to capture an impressive array of ordinary causal intuitions. However, in this paper I demonstrate how these abnormality-based accounts (...) still systematically fail to predict ordinary causal judgments in specific types of scenarios: those in which the effect is normal. I will argue that the reason for this is that the underlying psychological theory is wrong: the idea that intuitive actual causes are abnormal events is only partially correct. To model ordinary judgments more realistically, researchers working in this area must adopt a more plausible underlying psychological theory: the correspondence hypothesis about judgments of actual causation. One of the consequences of this correspondence hypothesis is that normal effects are judged to have normal causes. (shrink)

This paper discusses the constitution relation within the framework of the mechanistic approach to neurobiological explanation. It develops a regularity theory of constitution as an alternative to the manipulationist theory of constitution advocated by some of the proponents of the mechanistic approach. After the main problems of the manipulationist account of constitution have been reviewed, the regularity account is developed based on the notion of a minimal type relevance theory. A minimal type relevance theory expresses a minimally necessary condition of (...) a given type that consists of a disjunction of minimally sufficient conditions of that type. Afterwards, the attained definition is successfully applied in an analysis of the logical structure of the explanation of spatial learning and memory in rats as a paradigm neurobiological explanation. The overall result is a more robust and more precise version of the mechanistic approach to neurobiological explanations. (shrink)

Counterfactual conditionals have been appealed to in various ways to show how the mind can be causally efficacious. However, it has often been overestimated what the truth of certain counterfactuals actually indicates about causation. The paper first identifies four approaches that seem to commit precisely this mistake. The arguments discussed involve erroneous assumptions about the connection of counterfactual dependence and genuine causation, as well as a disregard of the requisite evaluation conditions of counterfactuals. In a second step, the paper uses (...) the insights of the foregoing analyses to formulate a set of counterfactuals-based conditions that are characterized as sufficient to establish singular causal claims. The paper concludes that there are ample reasons to believe that some mental events satisfy all these conditions with respect to certain further events and, hence, that mental events sometimes are causes. (shrink)

There are, by now, many rival, sophisticated philosophical accounts of causation that qualify as ‘metaphysically reductive’. This is a good thing: these collective efforts have vastly improved our understanding of causation over the last 30 years or so. They also put us in an excellent position to reflect on some central methodological questions: What exactly is the point of offering a metaphysical reduction of causation? What philosophical scruples ought to guide the pursuit of such a reduction? Finally, how should answers (...) to these latter questions affect one’s assessment of the main contemporary approaches? That’s what we investigate in this essay. Section 1 lays out our presuppositions. Section 2 reviews a sample of philosophical accounts. Then comes the main event: Section 3 looks in detail at the foregoing methodological questions, closing with a reconsideration of our sample accounts, in light of what we’ve found. (shrink)

Recent work in psychology and experimental philosophy has shown that judgments of actual causation are often influenced by consideration of defaults, typicality, and normality. A number of philosophers and computer scientists have also suggested that an appeal to such factors can help deal with problems facing existing accounts of actual causation. This article develops a flexible formal framework for incorporating defaults, typicality, and normality into an account of actual causation. The resulting account takes actual causation to be both graded and (...) comparative. We then show how our account would handle a number of standard cases. 1 Introduction2 Causal Models3 The HP Definition of Actual Causation4 The Problem of Isomorphism5 Defaults, Typicality, and Normality6 Extended Causal Models7 Examples7.1 Omissions7.2 Knobe effects7.3 Causes versus background conditions7.4 Bogus prevention7.5 Causal chains7.6 Legal doctrines of intervening causes7.7 Pre-emption and short circuits8 Conclusion. (shrink)

Judea Pearl (2000) was the first to propose a definition of actual causation using causal models. A number of authors have suggested that an adequate account of actual causation must appeal not only to causal structure but also to considerations of normality. In Halpern and Hitchcock (2011), we offer a definition of actual causation using extended causal models, which include information about both causal structure and normality. Extended causal models are potentially very complex. In this study, we show how it (...) is possible to achieve a compact representation of extended causal models. (shrink)

Concepts of cause, choice, and information are closely related. A cause is a choice that can be held responsible. It is a difference that makes a difference. Information about past causes and their effects is a valuable commodity because it can be used to guide future choices. Information about criteria of choice is generated by choosing a subset from an ensemble for ‘reasons’ and has meaning for an interpreter when it is used to achieve an end. Natural selection evolves interpreters (...) with ends. Surviving genes embody a textual record of past choices that had favorable outcomes. Consultation of these archives guides current choices. Purposive choice is well-informed difference making. (shrink)

How do people judge the degree of causal responsibility that an agent has for the outcomes of her actions? We show that a relatively unexplored factor -- the robustness of the causal chain linking the agent’s action and the outcome -- influences judgments of causal responsibility of the agent. In three experiments, we vary robustness by manipulating the number of background circumstances under which the action causes the effect, and find that causal responsibility judgments increase with robustness. In the first (...) experiment, the robustness manipulation also raises the probability of the effect given the action. Experiments 2 and 3 control for probability-raising, and show that robustness still affects judgments of causal responsibility. In particular, Experiment 3 introduces an Ellsberg type of scenario to manipulate robustness, while keeping the conditional probability and the skill deployed in the action fixed. Experiment 4, replicates the results of Experiment 3, while contrasting between judgments of causal strength and of causal responsibility. The results show that in all cases, the perceived degree of responsibility increases with the robustness of the action-outcome causal chain. (shrink)

The Culpable Control Model assumes that causal judgments are irrational: a negative evaluative reaction to an agent would lead individuals to overestimate his causal contribution to a harm. However, the extent to which these judgments deviate from criteria of rationality remains unclear. The two present studies aimed at investigating conditions under which this effect occurs. Participants red a vignette in which the evaluative reaction was operationalized through the agent’s motives (blameworthy, laudable). We also varied the causal link between the agent’s (...) action and the outcome (strong, weak, preempted). In both studies, we found that negative motives influenced causal judgments when a strong action-outcome link could be established. In Study 2, we replicated the effect of motives on causal judgment in the weak link condition. However, participants consistently judged the agent as less causal when the link was weak (vs. strong), regardless of the agent’s motives. These findings show that both the presence and the strength of the actual causal link between the action and the outcome moderate the detrimental effect of negative evaluative reactions on causal judgments. Thus, common sense causal judgments seem to deviate relatively little from the criteria of rationality. (shrink)

In Making Things Happen, James Woodward influentially combines a causal modeling analysis of actual causation with an interventionist semantics for the counterfactuals encoded in causal models. This leads to circularities, since interventions are defined in terms of both actual causation and interventionist counterfactuals. Circularity can be avoided by instead combining a causal modeling analysis with a semantics along the lines of that given by David Lewis, on which counterfactuals are to be evaluated with respect to worlds in which their antecedents (...) are realized by miracles. I argue, pace Woodward, that causal modeling analyses perform just as well when combined with the Lewisian semantics as when combined with the interventionist semantics. Reductivity therefore remains a reasonable hope. (shrink)

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)

An influential tradition in the philosophy of causation has it that all token causal facts are, or are reducible to, facts about difference-making. Challenges to this tradition have typically focused on pre-emption cases, in which a cause apparently fails to make a difference to its effect. However, a novel challenge to the difference-making approach has recently been issued by Alyssa Ney. Ney defends causal foundationalism, which she characterizes as the thesis that facts about difference-making depend upon facts about physical causation. (...) She takes this to imply that causation is not fundamentally a matter of difference-making. In this paper, I defend the difference-making approach against Ney’s argument. I also offer some positive reasons for thinking, pace Ney, that causation is fundamentally a matter of difference-making. (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)

According to the theory developed here, we may trace out the processes emanating from a cause in such a way that any consequence lying along one of these processes counts as an effect of the cause. This theory gives intuitive verdicts in a diverse range of problem cases from the literature. Its claims about causation will never be retracted when we include additional variables in our model. And it validates some plausible principles about causation, including Sartorio's ‘Causes as Difference Makers’ (...) principle and Hitchcock's ‘Principle of Sufficient Reason’. (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)

Medical interventions that cure or prevent medical conditions are central to medicine; and thus, understanding them is central to our understanding of medicine. My purpose in this paper is to explore the conceptual foundations of medicine by providing a singular analysis of the concept of a ‘preventive or curative medical intervention’. Borrowing a general account of prevention from Phil Dowe, I provide an analysis of prevention, cure, risk reduction, and a preventive or curative intervention, before turning to preventive and curative (...) medical interventions. The resulting counterfactual-mechanistic account holds that preventive and curative medical interventions reduce the probability of a medical condition in an actual population compared to their counterfactual omission, commonly by disrupting an etiological or constitutive mechanism for the condition. (shrink)

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)

Fundamental physics makes no clear use of causal notions; it uses laws that operate in relevant respects in both temporal directions and that relate whole systems across times. But by relating causation to evidence, we can explain how causation fits in to a physical picture of the world and explain its temporal asymmetry. This paper takes up a deliberative approach to causation, according to which causal relations correspond to the evidential relations we need when we decide on one thing in (...) order to achieve another. Tamsin's taking her umbrella is a cause of her staying dry, for example, if and only if her deciding to take her umbrella for the sake of staying dry is adequate grounds for believing she'll stay dry. This correspondence explains why causation matters: knowledge of causal structure helps us make decisions that are evidence of outcomes we seek. The account also explains why we can control the future and not the past, and why causes come before their effects. When agents properly deliberate, their decisions can never count as evidence for any outcomes they may seek in the past. From this it follows that causal relations don't run backwards. This deliberative asymmetry is itself traced back to asymmetries of evidence and entropy, providing a new way of deriving causal asymmetry from temporally symmetric laws. (shrink)

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.

ABSTRACT 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. 1Introduction 2Preemption 3Structural Equation Models 4The Halpern and Pearl Definition of ‘Actual Cause’ 5Preemption Again 6The Probabilistic Case 7Probabilistic Causal Models 8A Proposed Probabilistic Extension of Halpern and Pearl’s Definition 9Twardy and Korb’s Account 10Probabilistic (...) Fizzling 11Conclusion. (shrink)

Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...) psychology. Despite the ubiquity of causal reasoning, textbooks of cognitive psychology have neglected this growing field. The goal of The Oxford Handbook of Causal Reasoning is to fill this gap. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields. 1. Causal Reasoning: An Introduction Michael R. Waldmann Part I: Theories of Causal Cognition 2. Associative Accounts of Causal Cognition Mike E. Le Pelley, Oren Griffiths, and Tom Beesley 3. Rules of Causal Judgment: Mapping Statistical Information Onto Causal Beliefs José C. Perales, Andrés Catena, Antonio Cándido, and Antonio Maldonado 4. The Inferential Reasoning Theory of Causal Learning: Toward a Multi- Process Propositional Account Yannick Boddez, Jan De Houwer, and Tom Beckers 5. Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World: Generalizing the Invariance of Causal Power Patricia W. Cheng and Hongjing Lu 6. The Acquisition and Use of Causal Structure Knowledge Benjamin Margolin Rottman 7. Formalizing Prior Knowledge in Causal Induction Thomas L. Griffiths 8. Causal Mechanisms Samuel G. B. Johnson and Woo-kyoung Ahn 9. Force Dynamics and Causation Phillip Wolff and Robert Thorstad 10. Mental Models and Causation P. N. Johnson- Laird and Sangeet S. Khemlani 11. Pseudocontingencies Klaus Fiedler and Florian Kutzner 12. Singular Causation David Danks 13. Cognitive Neuroscience of Causal Reasoning Joachim T. Operskalski and Aron K. Barbey Part II: Basic Cognitive Functions 14. Visual Impressions of Causality Peter White 15. Goal-Directed Actions Bernhard Hommel 16. Planning and Control Magda Osman 17. Reinforcement Learning and Causal Models Samuel J. Gershman 18. Causation and the Probability of Causal Conditionals David E. Over 19. Causal Models and Conditional Reasoning Mike Oaksford and Nick Chater 20. Concepts as Causal Models: Categorization Bob Rehder 21. Concepts as Causal Models: Induction Bob Rehder 22. Causal Explanation Tania Lombrozo and Nadya Vasilyeva 23. Diagnostic Reasoning Björn Meder and Ralf Mayrhofer 24. Inferring Causal Relations by Analogy Keith J. Holyoak and Hee-Seung Lee 25. Causal Argument Ulrike Hahn, Roland Bluhm, and Frank Zenker 26. Causality in Decision- Making York Hagmayer and Philip M. Fernbach Part III: Domains of Causal Reasoning 27. Intuitive Theories Tobias Gerstenberg and Joshua B. Tenenbaum 28. Space, Time, and Causality Marc J. Buehner 29. Causation in Legal and Moral Reasoning David A. Lagnado and Tobias Gerstenberg 30. The Role of Causal Knowledge in Reasoning About Mental Disorders Woo-kyoung Ahn, Nancy S. Kim, and Matthew S. Lebowitz 31. Causality and Causal Reasoning in Natural Language Torgrim Solstad and Oliver Bott 32. Social Attribution and Explanation Denis Hilton Part IV: Development, Phylogeny, and Culture 33. The Development of Causal Reasoning Paul Muentener and Elizabeth Bonawitz 34. Causal Reasoning in Non-Human Animals Christian Schloegl and Julia Fischer 35. Causal Cognition and Culture Andrea Bender, Sieghard Beller, and Douglas L. Medin. (shrink)

This dissertation begins by addressing the question of when a causal model is apt for deciding questions of actual causation with respect to some target situation. I first provide relevant background about causal models, explain what makes them promising as a tool for analyzing actual causation, and motivate the need for a theory of aptness as part of such an analysis (Chapter 1). I then define what it is for a model on a given interpretation to be accurate of, that (...) is, say only true things about, some target situation. This involves a systematization of various representational principles mentioned and/or discussed throughout the literature into a method of interpretation, which I propose be taken as standard (Chapter 2). Next, I explain and address two reasons for which accuracy as I’ve defined it is insufficient for aptness. The first reason – already discussed in the literature – is the problem of structural isomorphs. In response, I propose the aptness condition of Explicit Partial Mediation (Chapter 3). The second reason – which has yet to be noticed – is the problem of the indeterminacy of accuracy. As I demonstrate, a model is accurate of a target situation only relative to a set of background possibilities – what I call a modal profile. It follows that a model represents a situation only relative to some modal profile or other. I go on to discuss the ramifications of this observation for a theory of actual causation in terms of models. I argue that the relativity be taken at face value and built into our metaphysical account of causation, resulting in a view that I call causal relativism (Chapter 4). I explore one advantage of this view in detail: that the resulting account can defend the principle of strong proportionality against several objections (Chapter 5). Finally, I apply the earlier discussion of aptness to attempts to provide a semantics of counterfactuals in terms of causal models – an interventionist semantics. I show how just as a similarity semantics relies on an opaque notion of similarity, an interventionist semantics relies on an analogous notion of aptness. The challenge of articulating aptness thus undermines the claim that an interventionist semantics avoids representational problems inherent in a similarity semantics (Chapter 6). I close with a recap and suggestions for future research (Chapter 7). -/- . (shrink)

Manipulablity theories of causation, according to which causes are to be regarded as handles or devices for manipulating effects, have considerable intuitive appeal and are popular among social scientists and statisticians. This article surveys several prominent versions of such theories advocated by philosophers, and the many difficulties they face. Philosophical statements of the manipulationist approach are generally reductionist in aspiration and assign a central role to human action. These contrast with recent discussions employing a broadly manipulationist framework for understanding causation, (...) such as those due to the computer scientist Judea Pearl and others, which are non-reductionist and rely instead on the notion of an intervention. This is simply an appropriately exogenous causal process; it has no essential connection with human action. This interventionist framework manages to avoid at least some of these difficulties faced by traditional philosophical versions of the manipulability theory and helps to clarify the content of causal claims. (shrink)

Questions about the metaphysics of causation may be usefully divided as follows. First, there are questions about the nature of the causal relata, including (1.1) whether they are in spacetime immanence), (1.2) how fine grained they are individuation), and (1.3) how many there are adicity). Second, there are questions about the metaphysics of the causal relation, including (2.1) what is the difference between causally related and causally unrelated sequences connection), (2.2) what is the difference between sequences related as cause to (...) effect, and those related as effect to cause or as joint effects of a common cause direction), and (2.3) what is the difference between sequences involving the cause, and those involving mere conditions selection). (shrink)

The basic idea of counterfactual theories of causation is that the meaning of causal claims can be explained in terms of counterfactual conditionals of the form “If A had not occurred, C would not have occurred”. While counterfactual analyses have been given of type-causal concepts, most counterfactual analyses have focused on singular causal or token-causal claims of the form “event c caused event e”. Analyses of token-causation have become popular in the last thirty years, especially since the development in the (...) 1970's of possible world semantics for counterfactuals. The best known counterfactual analysis of causation is David Lewis's (1973b) theory. However, intense discussion over thirty years has cast doubt on the adequacy of any simple analysis of singular causation in terms of counterfactuals. Recent years have seen a proliferation of different refinements of the basic idea to achieve a closer match with commonsense judgements about causation. (shrink)

Consider the following claims: -/- 1. The drought caused the famine. -/- 2. Drowsy driving causes crashes. -/- 3. How much I water my plant influences how tall it grows. -/- 4. How much novocaine a patient receives affects how much pain they will feel during dental surgery. -/- The metaphysics of causation asks questions about what it takes for claims like these to be true—what kind of relation the claims are about, and in virtue of what these relations obtain.

Using structural equations and directed graphs, Christopher Hitchcock (2007a) proposes a theory specifying the circumstances in which counterfactual dependence of one event e on another event c is necessary and sufficient for c to count as an actual cause of e. In this paper, we argue that Hitchcock is committed to a widely-endorsed folk attribution desideratum (FAD) for theories of actual causation. We then show experimentally that Hitchcock’s theory does not satisfy the FAD, and hence, it is in need of (...) revision. (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.

Whether there exist causal relations between guns firing and people dying, between pedals pressed and cars accelerating, or between carbon dioxide emissions and global warming, is typically taken to be a mind-independent, objective, matter of fact. However, recent contributions to the literature on causation, in particular theories of contrastive causation and causal modelling, have undermined this central causal platitude by relativising causal facts to models or to interests. This thesis flies against the prevailing wind by arguing that we must pay (...) greater attention to which elements of our causal talk vary with context and which elements track genuine features of the world around us. I will argue that once these elements are teased apart we will be in a position to better understand some of the most persistent problems in the philosophy of causation: pre-emption cases, absence causation, failures of transitivity and overdetermination. The result is a naturalist account of causation, concordant with the contextual variability we find in our ordinary causal talk, and parsimonious with respect to the theoretical entities posited. (shrink)

This thesis studies Judea Pearl’s logic of counterfactuals derived from the causal modeling framework, in comparison to the influential Stanlnaker-Lewis counterfactual logics. My study focuses on a characteristic principle in Pearl’s logic, named reversibility. The principle, as Pearl pointed out, goes beyond Lewis’s logic. Indeed, it also goes beyond the stronger logic of Stanlnaker, which is more analogous to Pearl’s logic. The first result of this thesis is an extension of Stanlnaker’s logic incorporating reversibility. It will be observed that the (...) translation of reversibility from Pearl’s language to the standard language for conditional logic deserves some attention. In particular, a straightforward translation following Pearl’s suggestion would render reversibility incompatible with Stanlnaker’s logic. A new translation of reversibility will be proposed, and an extension of Stanlnaker’s logic with the inclusion of the translated reversibility will be investigated. More importantly, it will be shown that the extended Stanlnaker’s logic is sound and complete with respect to a modified Stanlnaker’s semantics. The extension of Stanlnaker’s logic has an interesting implication. Zhang, Lam, and de Clercq have shown that special case of reversibility, despite its name, actually states an important kind of irreversibility: counterfactual dependence between distinct events is irreversible. In other words, reversibility entails that there is no cycle of counterfactual dependence altogether generalizations of reversibility. However, as shown in Zhang et al., Pearls’ logic does not rule out cycles of counterfactual dependence altogether. It in fact allows cycles that involve three or more distinct events. This is peculiar because the status of cyclic counterfactual dependence seems no more metaphysically secure than that of mutual counterfactual dependence. This consideration leads to an exploration of logics that rule out all cycles of counterfactual dependence. A surprising result is that the extension of Stanlnaker’s logic is precisely a logic of this sort. (shrink)

In this paper, we consider three competing explanations of the empirical finding that people’s causal attributions are responsive to normative details, such as whether an agent’s action violated an injunctive norm—the intervention view, the bias view, and the responsibility view. We then present new experimental evidence concerning a type of case not previously investigated in the literature. In the switch version of the trolley problem, people judge that the bystander ought to flip the switch, but they also judge that she (...) is more responsible for the resulting outcome when she does so than when she refrains. And, as predicted by the responsibility view, but not the intervention or bias views, people are more likely to say that the bystander caused the outcome when she flips the switch. (shrink)

A common objection to probabilistic theories of causation is that there are prima facie causes that lower the probability of their effects. Among the many replies to this objection, little attention has been given to Mellor's (1995) indirect strategy to deny that probability-lowering factors are bona fide causes. According to Mellor, such factors do not satisfy the evidential, explanatory, and instrumental connotations of causation. The paper argues that the evidential connotation only entails an epistemically relativized form of causal attribution, not (...) causation itself, and that there are clear cases of explanation and instrumental reasoning that must appeal to negatively relevant factors. In the end, it suggests a more liberal interpretation of causation that restores its connotations. Una objeción común a las teorías probabilísticas de la causalidad es que aparentemente existen causas que disminuyen la probabilidad de sus efectos. Entre las muchas respuestas a esta objeción, se le ha dado poca atención a la estrategia indirecta de D. H. Mellor (1995) para negar que un factor que disminuya la probabilidad de un efecto sea una causa legítima. Según Mellor, tales factores no satisfacen las connotaciones evidenciales, explicativas e instrumentales de la causalidad. El artículo argumenta que la connotación evidencial sólo implica una forma epistémicamente relativizada de atribución causal y no la causalidad misma, y que hay casos claros de explicación y razonamiento instrumental que deben apelar a factores negativamente relevantes. Se sugiere una interpretación más liberal de la causalidad que reinstaura sus connotaciones. (shrink)