Both philosophers and psychologists have argued for the existence of distinct kinds of explanations, including teleological explanations that cite functions or goals, and mechanistic explanations that cite causal mechanisms. Theories of causation, in contrast, have generally been unitary, with dominant theories focusing either on counterfactual dependence or on physical connections. This paper argues that both approaches to causation are psychologically real, with different modes of explanation promoting judgments more or less consistent with each approach. Two sets of experiments isolate the (...) contributions of counterfactual dependence and physical connections in causal ascriptions involving events with people, artifacts, or biological traits, and manipulate whether the events are construed teleologically or mechanistically. The findings suggest that when events are construed teleologically, causal ascriptions are sensitive to counterfactual dependence and relatively insensitive to the presence of physical connections, but when events are construed mechanistically, causal ascriptions are sensitive to both counterfactual dependence and physical connections. The conclusion introduces an account of causation, an "exportable dependence theory," that provides a way to understand the contributions of physical connections and teleology in terms of the functions of causal ascriptions. (shrink)

This paper explores some issues about the choice of variables for causal representation and explanation. Depending on which variables a researcher employs, many causal inference procedures and many treatments of causation will reach different conclusions about which causal relationships are present in some system of interest. The assumption of this paper is that some choices of variables are superior to other choices for the purpose of causal analysis. A number of possible criteria for variable choice are described and defended within (...) a broadly interventionist approach to causation. (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)

The past decade has seen a renewed interest in moral psychology. A unique feature of the present endeavor is its unprecedented interdisciplinarity. For the first time, cognitive, social, and developmental psychologists, neuroscientists, experimental philosophers, evolutionary biologists, and anthropologists collaborate to study the same or overlapping phenomena. This review focuses on moral judgments and is written from the perspective of cognitive psychologists interested in theories of the cognitive and affective processes underlying judgments in moral domains. The review will first present and (...) discuss a variety of different theoretical and empirical approaches, including both behavioral and neuroscientific studies. We will then show how these theories can be applied to a selected number of specific research topics that have attracted particular interest in recent years, including the distinction between moral and conventional rules, moral dilemmas, the role of intention, and sacred/protected values. One overarching question we will address throughout the chapter is whether moral cognitions are distinct and special, or whether they can be subsumed under more domain-general mechanisms. (shrink)

We report three experiments investigating whether people’s judgments about causal relationships are sensitive to the robustness or stability of such relationships across a range of background circumstances. In Experiment 1, we demonstrate that people are more willing to endorse causal and explanatory claims based on stable (as opposed to unstable) relationships, even when the overall causal strength of the relationship is held constant. In Experiment 2, we show that this effect is not driven by a causal generalization’s actual scope of (...) application. In Experiment 3, we offer evidence that stable causal relationships may be seen as better guides to action. Collectively, these experiments document a previously underappreciated factor that shapes people’s causal reasoning: the stability of the causal relationship. (shrink)

Abstract“Force dynamics” refers to a previously neglected semantic category—how entities interact with respect to force. This category includes such concepts as: the exertion of force, resistance to such exertion and the overcoming of such resistance, blockage of a force and the removal of such blockage, and so forth. Force dynamics is a generalization over the traditional linguistic notion of “causative”: it analyzes “causing” into finer primitives and sets it naturally within a framework that also includes “letting,”“hindering,”“helping,” and still further notions. (...) Force dynamics, moreover, appears to be the semantic category that uniquely characterizes the grammatical category of modals, in both their basic and epistemic usages. In addition, on the basis of force dynamic parameters, numerous lexical items fall into systematic semantic patterns, and there exhibit parallelisms between physical and psychosocial reference. Further, from research on the relation of semantic structure to general cognitive structure, it appears that the concepts of force interaction that are encoded within language closely parallel concepts that appear both in early science and in naive physics and psychology. Overall, force dynamics thus emerges as a fundamental notional system that structures conceptual material pertaining to force interaction in a common way across a linguistic range: the physical, psychological, social, inferential, discourse, and mental-model domains of reference and conception. (shrink)

Causal queries about singular cases, which inquire whether specific events were causally connected, are prevalent in daily life and important in professional disciplines such as the law, medicine, or engineering. Because causal links cannot be directly observed, singular causation judgments require an assessment of whether a co‐occurrence of two events c and e was causal or simply coincidental. How can this decision be made? Building on previous work by Cheng and Novick (2005) and Stephan and Waldmann (2018), we propose a (...) computational model that combines information about the causal strengths of the potential causes with information about their temporal relations to derive answers to singular causation queries. The relative causal strengths of the potential cause factors are relevant because weak causes are more likely to fail to generate effects than strong causes. But even a strong cause factor does not necessarily need to be causal in a singular case because it could have been preempted by an alternative cause. We here show how information about causal strength and about two different temporal parameters, the potential causes' onset times and their causal latencies, can be formalized and integrated into a computational account of singular causation. Four experiments are presented in which we tested the validity of the model. The results showed that people integrate the different types of information as predicted by the new model. (shrink)

The authors challenge the reigning “causal power framework” as an explanation for whether a particular outcome was actually caused by a specific potential cause. They test a new measure of causal attribution in two experiments by embedding the measure within the Structure Induction model of Singular Causation (SISC, Stephan & Waldmann, 2016).

Short abstract (98 words). Reasoning is generally seen as a means to improve knowledge and make better decisions. However, much evidence shows that reasoning often leads to epistemic distortions and poor decisions. This suggests that the function of reasoning should be rethought. Our hypothesis is that the function of reasoning is argumentative. It is to devise and evaluate arguments intended to persuade. Reasoning so conceived is adaptive given humans’ exceptional dependence on communication and vulnerability to misinformation. A wide range of (...) evidence in the psychology of reasoning and decision making can be reinterpreted and better explained in the light of this hypothesis. (shrink)

The verbs cause, enable, and prevent express beliefs about the way the world works. We offer a theory of their meaning in terms of the structure of those beliefs expressed using qualitative properties of causal models, a graphical framework for representing causal structure. We propose that these verbs refer to a causal model relevant to a discourse and that “A causes B” expresses the belief that the causal model includes a link from A to B. “A enables/allows B” entails that (...) the model includes a link from A to B, that A represents a category of events necessary for B, and that an alternative cause of B exists. “A prevents B” entails that the model includes a link from A to B and that A reduces the likelihood of B. This theory is able to account for the results of four experiments as well as a variety of existing data on human reasoning. (shrink)

What conception of causation is at work in the law? I argue that the law implicitly relies on a contrastive conception. In a liability case where the defendant's breach of duty must be shown to have caused the plaintiff's damages, it is not enough to consider what would have happened if the cause had not occurredthe law requires us to look to a specific replacement for the effect, which in this case is the hypothetical outcome in which the plaintiff came (...) off better. In place of I suggest the more explicit An explicitly contrastive approach can thus potentially help the lawyer phrase her causal question in a more explicit way, while shedding light on our conception of causation. (shrink)

The physical and/or intrinsic connection approach to causation has become prominent in the recent literature, with Salmon, Dowe, Menzies, and Armstrong among its leading proponents. I show that there is a type of causation, causation by disconnection, with no physical or intrinsic connection between cause and effect. Only Hume-style conditions approaches and hybrid conditions-connections approaches appear to be able to handle causation by disconnection. Some Hume-style, extrinsic, absence-relating, necessary and/or sufficient condition component of the causal relation proves to be needed.

Causation is widely assumed to be a binary relation: c causes e. I will argue that causation is a quaternary, contrastive relation: c rather than C* causes e rather than E*, where C* and E* are nonempty sets of contrast events. Or at least, I will argue that treating causation as contrastive helps resolve some paradoxes.

This paper presents a drastically revised version of the theory of causality, based on analyses of causal processes and causal interactions, advocated in Salmon (1984). Relying heavily on modified versions of proposals by P. Dowe, this article answers penetrating objections by Dowe and P. Kitcher to the earlier theory. It shows how the new theory circumvents a host of difficulties that have been raised in the literature. The result is, I hope, a more satisfactory analysis of physical causality.

Scientific explanations must bear the proper relationship to the world: they must depict what, out in the world, is responsible for the explanandum. But explanations must also bear the proper relationship to their audience: they must be able to create human understanding. With few exceptions, philosophical accounts of explanation either ignore entirely the relationship between explanations and their audience or else demote this consideration to an ancillary role. In contrast, I argue that considering an explanation’s communicative role is crucial to (...) any satisfactory account of explanation. (shrink)

We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficulties in the traditional account. 1. Introduction2. Causal models: a review2.1Causal models2.2Syntax and semantics3. The definition of cause4. Examples5. A more refined definition6. DiscussionAAppendix: Some Technical IssuesA.1The active causal processA.2A closer look at AC2(b)A.3Causality with infinitely many variablesA.4Causality in nonrecursive (...) models. (shrink)

Judea Pearl (2000) has recently advanced a theory of token causation using his structural equations approach. This paper examines some counterexamples to Pearl's theory, and argues that the theory can be modified in a natural way to overcome them.

Some omissions seem to be causes. For example, suppose Barry promises to water Alice’s plant, doesn’t water it, and that the plant then dries up and dies. Barry’s not watering the plant – his omitting to water the plant – caused its death. But there is reason to believe that if omissions are ever causes, then there is far more causation by omission than we ordinarily think. In other words, there is reason to think the following thesis true.

Folk theories—untutored people’s (often implicit) theories about various features of the world—have been fashionable objects of inquiry in psychology for almost two decades now (e.g., Hirschfeld and Gelman 1994), and more recently they have been of interest in experimental philosophy (Nichols 2004). Folk theories of psy- chology, physics, biology, and ethics have all come under investigation. Folk meta- physics, however, has not been as extensively studied. That so little is known about folk metaphysics is unfortunate for (at least) two reasons. (...) First, folk metaphysics is almost certainly implicit, and it is likely to be our default way of thinking about metaphysical problems. Moreover, one’s metaphysical commitments can have pro- found consequences—in scientific, religious, and ethical contexts, for example. Thus, folk metaphysics ought to be dragged out into the open and exposed to criticism. As Peirce eloquently remarked (1994, 1.129; see also 1994, 7.579). (shrink)

Several prominent, contemporary theories of actual causation maintain that in order for something to count as an actual cause (in the circumstances) of some known effect, the potential cause must be a difference-maker with respect to the effect in some restricted range of circumstances. Although the theories disagree about how to restrict the range of circumstances that must be considered in deciding whether something counts as an actual cause of a known effect, the theories agree that at least some counterfactual (...) circumstances must be considered. I argue that the theories are still too permissive in the range of counterfactual circumstances they admit for consideration, and I present simple counter-examples that make use of this overpermissiveness. (shrink)

How do people attribute responsibility in situations where the contributions of multiple agents combine to produce a joint outcome? The prevalence of over-determination in such cases makes this a difficult problem for counterfactual theories of causal responsibility. In this article, we explore a general framework for assigning responsibility in multiple agent contexts. We draw on the structural model account of actual causation (e.g., Halpern & Pearl, 2005) and its extension to responsibility judgments (Chockler & Halpern, 2004). We review the main (...) theoretical and empirical issues that arise from this literature and propose a novel model of intuitive judgments of responsibility. This model is a function of both pivotality (whether an agent made a difference to the outcome) and criticality (how important the agent is perceived to be for the outcome, before any actions are taken). The model explains empirical results from previous studies and is supported by a new experiment that manipulates both pivotality and criticality. We also discuss possible extensions of this model to deal with a broader range of causal situations. Overall, our approach emphasizes the close interrelations between causality, counterfactuals, and responsibility attributions. (shrink)

Causal judgments are widely known to be sensitive to violations of both prescriptive norms (e.g., immoral events) and statistical norms (e.g., improbable events). There is ongoing discussion as to whether both effects are best explained in a unified way through changes in the relevance of counterfactual possibilities, or whether these two effects arise from unrelated cognitive mechanisms. Recent work has shown that moral norm violations affect causal judgments of agents, but not inanimate artifacts used by those agents. These results have (...) been interpreted as showing that prescriptive norm violations only affect causal reasoning about intentional agents, but not the use of inanimate artifacts, thereby providing evidence that the effect of prescriptive norm violations arises from mechanisms specific to reasoning about intentional agents, and thus casting doubt on a unified counterfactual analysis of causal reasoning. Four experiments explore this recent finding and provide clear support for a unified counterfactual analysis. Experiment 1 demonstrates that these newly observed patterns in causal judgments are closely mirrored by judgments of counterfactual relevance. Experiment 2 shows that the relationship between causal and counterfactual judgments is moderated by causal structure, as uniquely predicted by counterfactual accounts. Experiment 3 directly manipulates the relevance of counterfactual alternatives and finds that causal judgments of intentional agents and inanimate artifacts are similarly affected. Finally, Experiment 4 shows that prescriptive norm violations (in which artifacts malfunction) affect causal judgments of inanimate artifacts in much the same way that prescriptive norm violations (in which agents act immorally) affect causal judgments of intentional agents. (shrink)

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)

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)

This article describes research pursued by members of the McDonnell Collaborative on Causal Learning. A number of members independently converged on a similar idea: one of the central functions served by claims of actual causation is to highlight patterns of dependence that are highly portable into novel contexts. I describe in detail how this idea emerged in my own work and also in that of the psychologist Tania Lombrozo. In addition, I use the occasion to reflect on the nature of (...) interdisciplinary collaboration in general and on the interaction between philosophy and psychology in particular. (shrink)

Much of the philosophical literature on causation has focused on the concept of actual causation, sometimes called token causation. In particular, it is this notion of actual causation that many philosophical theories of causation have attempted to capture.2 In this paper, we address the question: what purpose does this concept serve? As we shall see in the next section, one does not need this concept for purposes of prediction or rational deliberation. What then could the purpose be? We will argue (...) that one can gain an important clue here by looking at the ways in which causal judgments are shaped by people‘s understanding of norms. (shrink)

People generally accept that there is causation by omission—that the omission of some events cause some related events. But this acceptance elicits the selection problem, or the difficulty of explaining the selection of a particular omissive cause or class of causes from the causal conditions. Some theorists contend that dependence theories of causation cannot resolve this problem. In this paper, we argue that the appeal to norms adequately resolves the selection problem for dependence theories, and we provide novel experimental evidence (...) for it. (shrink)

People’s causal judgments are susceptible to the action effect, whereby they judge actions to be more causal than inactions. We offer a new explanation for this effect, the counterfactual explanation: people judge actions to be more causal than inactions because they are more inclined to consider the counterfactual alternatives to actions than to consider counterfactual alternatives to inactions. Experiment 1a conceptually replicates the original action effect for causal judgments. Experiment 1b confirms a novel prediction of the new explanation, the reverse (...) action effect, in which people judge inactions to be more causal than actions in overdetermination cases. Experiment 2 directly compares the two effects in joint-causation and overdetermination scenarios and conceptually replicates them with new scenarios. Taken together, these studies provide support for the new counterfactual explanation for the action effect in causal judgment. (shrink)

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)

Structural equations have become increasingly popular in recent years as tools for understanding causation. But standard structural equations approaches to causation face deep problems. The most philosophically interesting of these consists in their failure to incorporate a distinction between default states of an object or system, and deviations therefrom. Exploring this problem, and how to fix it, helps to illuminate the central role this distinction plays in our causal thinking.

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)

We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficulties in the traditional account.

Many of our decisions pertain to causal systems. Nevertheless, only recently has it been claimed that people use causal models when making judgments, decisions and predictions, and that causal Bayes nets allow us to formally describe these inferences. Experimental research has been limited to simple, artificial problems, which are unrepresentative of the complex dynamic systems we successfully deal with in everyday life. For instance, in social interactions, we can explain the actions of other's on the fly and we can generalize (...) from limited observations to predict future actions and their consequences. Our main argument is that none of these inferences (i.e., induction, generalization, explanation, and prediction) can be achieved without causal reasoning. As a case in point we use the popular television series desperate housewives and show how causal Bayes nets are able to explain the inferences made in social contexts. Crucially, causal Bayes nets also allow us to understand why we can infer so much from so little when making sense of a protagonist's behavior. (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)

In some cases people judge it morally acceptable to sacrifice one person’s life in order to save several other lives, while in other similar cases they make the opposite judgment. Researchers have identified two general factors that may explain this phenomenon at the stimulus level: (1) the agent’s intention (i.e. whether the harmful event is intended as a means or merely foreseen as a side-effect) and (2) whether the agent harms the victim in a manner that is relatively “direct” or (...) “personal”. Here we integrate these two classes of findings. Two experiments examine a novel personalness/directness factor that we call personal force, present when the force that directly impacts the victim is generated by the agent’s muscles (e.g., in pushing). Experiments 1a and b demonstrate the influence of personal force on moral judgment, distinguishing it from physical contact and spatial proximity. Experiments 2a and b demonstrate an interaction between personal force and intention, whereby the effect of personal force depends entirely on intention. These studies also introduce a method for controlling for people’s real-world expectations in decisions involving potentially unrealistic hypothetical dilemmas. (shrink)

This paper develops the view that in arguing informally individuals construct a dual representation in which there is a coupling of arguments and the structure of the qualitative (mental) causal model to which these refer. Invited to consider a future possibility, individuals generate a causal model and mentally simulate the consequences of certain actions. Their arguments refer to the causal paths in the model. Correspondingly, faced with specific arguments about a policy option they generate a model with particular causal paths (...) and mentally simulate the outcomes. The results of Experiment 1 are consistent with this notion. Decisions on the percentage of funds to be allocated to genetically modified (GM) crop research depended on the structure of the arguments elicited in response to imagining a future state of affairs. Specifically, the presence of a dissuasive argument eliminated the impact of any persuasive argument. The non-monotonic properties of everyday informal argument can then be seen as a corollary of change to causal structure in the model. The dual representation view predicts that the impact of a dissuasive argument will depend on the structure of the causal model. Experiment 2 tested and confirmed this prediction by requiring individuals to judge the relative persuasiveness of two cases referring either to a model with two independent causal paths or to a model in which one causal path depended on the other. In contrast to Experiment 1, prior opinion on GM crop research did not affect allocation decisions. An advisory role in contrast to a participant role may encourage a more decontextualised mode of thinking. According to the dual representation view, ease of mental simulation should exert wide-ranging effects on judgements and the rhetoric of arguments should also be important. The paper concludes with a discussion of some of these expectations. (shrink)

This paper outlines a theory and computer implementation of causal meanings and reasoning. The meanings depend on possibilities, and there are four weak causal relations: A causes B, A prevents B, A allows B, and A allows not‐B, and two stronger relations of cause and prevention. Thus, A causes B corresponds to three possibilities: A and B, not‐A and B, and not‐A and not‐B, with the temporal constraint that B does not precede A; and the stronger relation conveys only the (...) first and last of these possibilities. Individuals represent these relations in mental models of what is true in the various possibilities. The theory predicts a number of phenomena, and, contrary to many accounts, it implies that the meaning of causation is not probabilistic, differs from the meaning of enabling conditions, and does not depend on causal powers or mechanisms. The theory also implies that causal deductions do not depend on schemas or rules. (shrink)

A Bayesian network (BN) is a graphical model of uncertainty that is especially well suited to legal arguments. It enables us to visualize and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs have been widely discussed and recently used in the context of legal arguments, there is no systematic, repeatable method for modeling legal arguments as BNs. Hence, where (...) BNs have been used in the legal context, they are presented as completed pieces of work, with no insights into the reasoning and working that must have gone into their construction. This means the process of building BNs for legal arguments is ad hoc, with little possibility for learning and process improvement. This article directly addresses this problem by describing a method for building useful legal arguments in a consistent and repeatable way. The method complements and extends recent work by Hepler, Dawid, and Leucari (2007) on object-oriented BNs for complex legal arguments and is based on the recognition that such arguments can be built up from a small number of basic causal structures (referred to as idioms). We present a number of examples that demonstrate the practicality and usefulness of the method. (shrink)