Here I consider the relative merits of two recent models of explanation, James Woodward's interventionist-counterfactual model and the model model. According to the former, explanations are largely constituted by information about the consequences of counterfactual interventions. Problems arise for this approach because countless relevant interventions are possible in most cases and because it overlooks other kinds of equally relevant information. According the model model, explanations are largely constituted by cognitive models of actual mechanisms. On this approach, (...) explanations tend not to represent any of the aforementioned information explicitly but can instead be used to produce it on demand. The model model thus offers the more plausible account of the information of which we are aware when we have an explanation and of the ratiocinative process through which we derive many kinds of information that are relevant to the evaluation of explanations. (shrink)

Frankfurt’s “Alternate Possibilities and Moral Responsibility” made an important intervention into the literature on moral responsibility via a classical Frankfurt-type example, arguing that “the principle of alternate possibilities” is false. This paper argues that classical Frankfurt-type examples fail due to the use of agentic counterfactual interventions who lack agency. Using finite state machines to illustrate, I show the models that classical Frankfurt-type examples must use and why they are incongruent with leeway incompatibilist beliefs—the motivating interlocutor for classical Frankfurt-type (...) examples. I then argue that returning agency to the agentic counterfactual intervention also returns alternate possibilities to the actual sequence of events, undermining a core premise of Frankfurt’s. Lastly, I show why a number of potential counterarguments should fail to persuade the leeway incompatibilist. (shrink)

Causal models show promise as a foundation for the semantics of counterfactual sentences. However, current approaches face limitations compared to the alternative similarity theory: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This article addresses these difficulties using exogenous interventions, where causal interventions change the values of exogenous variables rather than structural equations. This model accommodates judgments about backtracking counterfactuals, extends to logically complex counterfactuals, and validates (...) familiar principles of counterfactual logic. This combines the interventionist intuitions of the causal approach with the logical advantages of the similarity approach. (shrink)

Team semantics is a highly general framework for logics which describe dependencies and independencies among variables. Typically, the dependencies considered in this context are properties of sets of configurations or data records. We show how team semantics can be further generalized to support languages for the discussion of interventionist counterfactuals and causal dependencies, such as those that arise in manipulationist theories of causation. We show that the “causal teams” we introduce in the present paper can be used for modelling some (...) classical counterfactual scenarios which are not captured by the usual causal models. We then analyse the basic properties of our counterfactual languages and discuss extensively the differences with respect to the Lewisian tradition. (shrink)

Causal selection is the task of picking out, from a field of known causally relevant factors, some factors as elements of an explanation. The Causal Parity Thesis in the philosophy of biology challenges the usual ways of making such selections among different causes operating in a developing organism. The main target of this thesis is usually gene centrism, the doctrine that genes play some special role in ontogeny, which is often described in terms of information-bearing or programming. This paper is (...) concerned with the attempt of confronting the challenge coming from the Causal Parity Thesis by offering principles of causal selection that are spelled out in terms of an explicit philosophical account of causation, namely an interventionist account. I show that two such accounts that have been developed, although they contain important insights about causation in biology, nonetheless fail to provide an adequate reply to the Causal Parity challenge: Ken Waters's account of actual-difference making and Jim Woodward's account of causal specificity. A combination of the two also doesn't do the trick, nor does Laura Franklin-Hall's account of explanation (in this volume). We need additional conceptual resources. I argue that the resources we need consist in a special class of counterfactual conditionals, namely counterfactuals the antecedents of which describe biologically normal interventions. (shrink)

This article defends the use of interventionist counterfactuals to elucidate causal and explanatory claims against criticisms advanced by James Bogen and Peter Machamer. Against Bogen, I argue that counterfactual claims concerning what would happen under interventions are meaningful and have determinate truth values, even in a deterministic world. I also argue, against both Machamer and Bogen, that we need to appeal to counterfactuals to capture the notions like causal relevance and causal mechanism. Contrary to what both authors suppose, (...) counterfactuals are not "unscientific" - a substantial tradition within statistics and the causal modelling literature makes heavy use of them. (shrink)

Researchers, clinicians, and patients have good reasons for wanting answers to causal questions of disease and therapeutic intervention. This paper uses microbiologist Robert Koch’s pioneering work and famous postulates to extrapolate a logical sequence of evidence for confirming the causes of disease: association between individuals with and without a disease; isolation of causal agents; and the creation of a counterfactual. This paper formally introduces counter-counterfactuals, which appear to have been used, perhaps intuitively, since the time of Koch and possibly (...) earlier. An argument is presented that counter-counterfactuals are a useful tool for identifying necessary causes of disease, and sometimes must be used in place of isolation which is not always possible. In addition, a logical sequence of causal evidence for a therapeutic intervention is presented: creating a counterfactual, comparisons between subjects in receipt of treatment versus those who are not, and counter-counterfactuals. (shrink)

Counterfactual reasoning -- envisioning hypothetical scenarios, or possible worlds, where some circumstances are different from what (f)actually occurred (counter-to-fact) -- is ubiquitous in human cognition. Conventionally, counterfactually-altered circumstances have been treated as "small miracles" that locally violate the laws of nature while sharing the same initial conditions. In Pearl's structural causal model (SCM) framework this is made mathematically rigorous via interventions that modify the causal laws while the values of exogenous variables are shared. In recent years, however, this (...) purely interventionist account of counterfactuals has increasingly come under scrutiny from both philosophers and psychologists. Instead, they suggest a backtracking account of counterfactuals, according to which the causal laws remain unchanged in the counterfactual world; differences to the factual world are instead "backtracked" to altered initial conditions (exogenous variables). In the present work, we explore and formalise this alternative mode of counterfactual reasoning within the SCM framework. Despite ample evidence that humans backtrack, the present work constitutes, to the best of our knowledge, the first general account and algorithmisation of backtracking counterfactuals. We discuss our backtracking semantics in the context of related literature and draw connections to recent developments in explainable artificial intelligence (XAI). (shrink)

We report two Experiments to compare counterfactual thoughts about how an outcome could have been different and causal explanations about why the outcome occurred. Experiment 1 showed that people generate counterfactual thoughts more often about controllable than uncontrollable events, whereas they generate causal explanations more often about unexpected than expected events. Counterfactual thoughts focus on specific factors, whereas causal explanations focus on both general and specific factors. Experiment 2 showed that in their spontaneous counterfactual thoughts, people (...) focus on normal events just as often as exceptional events, unlike in directed counterfactual thoughts. The findings are consistent with the suggestion that counterfactual thoughts tend to focus on how a specific unwanted outcome could have been prevented, whereas causal explanations tend to provide more general causal information that enables future understanding, prediction, and intervention in a wide range of situations. (shrink)

Counterfactual analysis is an interesting feature of thought experiments, because it requires the imagination of alternative states of the world (see also publications by Fearon, Lebow and Stein, Reiss, and Tetlock and Belkin, who suggest the same). In historical analysis, the use of imagination is often the focus of criticisms of such counterfactual analysis. In this article, I consider three strategies for constraining imagination: making limited counterfactual changes, limiting counterfactual changes to the decisions of important figures, (...) and using evidence to restrict the scope for imagination. Given the focus of this special issue, I will relate this discussion to Lewis’s and Woodward’s analyses of counterfactuals in the philosophy of science. I show that counterfactual analysis in historical cases has some resemblance to Lewis’s and Woodward’s analyses, but that what Lewis calls “transition periods” cannot be left entirely vague, as Lewis suggests, nor can counterfactual changes be seen simply as interventions, as Woodward suggests. I propose that efforts to limit imagination in historical counterfactuals are ultimately problematic, but that imagination can nevertheless play a useful role in counterfactual analysis. (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)

The application of the formal framework of causal Bayesian Networks to children’s causal learning provides the motivation to examine the link between judgments about the causal structure of a system, and the ability to make inferences about interventions on components of the system. Three experiments examined whether children are able to make correct inferences about interventions on different causal structures. The first two experiments examined whether children’s causal structure and intervention judgments were consistent with one another. In Experiment (...) 1, children aged between 4 and 8 years made causal structure judgments on a three-component causal system followed by counterfactual intervention judgments. In Experiment 2, children’s causal structure judgments were followed by intervention judgments phrased as future hypotheticals. In Experiment 3, we explicitly told children what the correct causal structure was and asked them to make intervention judgments. The results of the three experiments suggest that the representations that support causal structure judgments do not easily support simple judgments about interventions in children. We discuss our findings in light of strong interventionist claims that the two types of judgments should be closely linked. (shrink)

Among the current philosophical attempts to understand causation two seem to be the most prominent. The first is James Woodward’s counterfactual approach; the second is the mechanistic approach advocated by Peter Machamer, Lindley Darden, Carl Craver, Jim Bogen and Stuart Glennan. The counterfactual approach takes it that causes make a difference to their effects, where this difference-making is cashed out in terms of actual and counterfactual interventions. The mechanistic approach takes it that two events are causally (...) related if and only if there is a mechanism that connects them. On the face of it, the two approaches need not be in conflict. The mechanisms might satisfy (or depend on) certain interventionist counterfactuals and, conversely, the interventionist counterfactuals might be made true by the presence of certain mechanisms. But, overall, both approaches tend to be imperialistic. Advocates of each argue that their own approach fairs much better than their opponents’. The question then is this: are we forced to choose between the mechanistic approach and the counterfactual one? In this paper, I argue that, as they stand, both approaches face some important problems that need to be fixed. I shall also argue that there is a sense in which the counterfactual approach is more basic than the mechanistic, though the former will benefit from a better understanding of the mechanisms that are at work in causal connections. So both approaches can work together to offer a better understanding of causation. If they work in tandem, they can offer us a glimpse of what Hume famously called “the secret connexion”. But in so far as the ‘secret connexion’ is an intrinsic relation between the causal relata, neither of the above approaches tells us what this relation is. (shrink)

This article uses the interventionist theory of causation, a counterfactual theory taken from philosophy of science, to strengthen causal analysis in process tracing research. Causal claims from process tracing are re-expressed in terms of so-called hypothetical interventions, and concrete evidential tests are proposed which are shown to corroborate process tracing claims. In particular, three steps are prescribed for an interventionist investigation, and each step in turn is shown to make the causal analysis more robust, amongst others by disambiguating (...) causal claims and clarifying or strengthening the existing methodological advice on counterfactual analysis. The article's claims are then illustrated using a concrete example, Haggard and Kaufman's analysis of the Argentinian transition to democracy. It is shown that interventionism could have strengthened the authors’ conclusions. The article concludes with a short Bayesian analysis of its key methodological proposals. (shrink)

This paper investigates what happens when we merge two different lines of theorizing about counterfactuals. One is the comparative closeness view, which was developed by Stalnaker and Lewis in the framework of possible worlds semantics. The second is the interventionist view, which is part of the causal models framework developed in statistics and computer science. Common lore and existing literature have it that the two views can be easily fit together, aside from a few details. I argue that, on the (...) contrary, transplanting causal-models-inspired ideas in a possible worlds framework yields a new semantics. The difference is grounded in different algorithms for handling inconsistent information, hence it touches on issues that are at the very heart of a semantics for contrary-to-fact conditionals. Roughly, Stalnaker/Lewis semantics requires us to evaluate the consequent of a counterfactual at all closest antecedent-verifying possibilities. Causal-models-based semantics also does this, but in addition uses the information contained in the antecedent, together with background causal information, to shift what worlds count as closest. This makes systematically different predictions and generates a new logic. The upshot is that we have a new semantics to study, and a substantial theoretical choice to make. (shrink)

Counterfactual causal interpretations of family genetic effects are appropriate, but neglect an important feature: Provision of unique information about expected outcomes following an independent decision, such as a decision to intervene. Counterfactual causality criteria are unlikely to resolve controversies about behavioral genetic findings; such controversies are likely to continue until counterfactual inferences are translated into interventional hypotheses and designs.

According to James Woodward’s influential interventionist account of causation, X is a cause of Y iff, roughly, there is a possible intervention on X that changes Y. Woodward requires that interventions be merely logically possible. I will argue for two claims against this modal character of interventions: First, merely logically possible interventions are dispensable for the semantic project of providing an account of the meaning of causal statements. If interventions are indeed dispensable, the interventionist theory collapses (...) into a counterfactual theory of causation. Thus, the interventionist theory is not tenable as a theory of causation in its own right. Second, if one maintains that merely logically possible interventions are indispensable, then interventions with this modal character lead to the fatal result that interventionist counterfactuals are evaluated inadequately. Consequently, interventionists offer an inadequate theory of causation. I suggest that if we are concerned with explicating causal concepts and stating the truth-conditions of causal claims we best get rid of Woodwardian interventions. (shrink)

We construct a causal-modeling semantics for both indicative and counterfactual conditionals. As regards counterfactuals, we adopt the orthodox view that a counterfactual conditional is true in a causal model M just in case its consequent is true in the submodel M∗, generated by intervening in M, in which its antecedent is true. We supplement the orthodox semantics by introducing a new manipulation called extrapolation. We argue that an indicative conditional is true in a causal model M just in (...) case its con- sequent is true in certain submodels M∗, generated by extrapolating M, in which its antecedent is true. We show that the proposed semantics can account for some important minimal pairs nicely and naturally. We also prove a theorem showing under what conditions intervention and extrapolation will yield the same result, and thus explain how counterfactual and indicative conditionals would behave in a causal-modeling semantics. (shrink)

Counterfactuals are inherently ambiguous in the sense that the same counterfactual may be true under one mode of counterfactualization but false under the other. Many have regarded the ambiguity of counterfactuals as consisting in the distinction between forward-tracking and backtracking counterfactuals. This is incorrect since the ambiguity persists even in cases not involving backtracking counterfactualization. In this paper, I argue that causal modeling semantics has the resources enough for accounting for the ambiguity of counterfactuals. Specifically, we need to distinguish (...) two types of causal manipulation, which I call “intervention” and “extrapolation” respectively. To intervene in a causal model M is to change M’s structural equations in some specific ways, while to extrapolate M is to change the value assignment of M’s variables in some specific ways. I argue that intervention and extrapolation offer a natural explanation for the ambiguity of counterfactuals. (shrink)

Counternomics—counterfactuals whose antecedents run contrary to the laws of nature—are commonplace in science but have enjoyed relatively little philosophical attention. This article discusses a puzzle about our counternomic epistemology, focusing on cases in which experimental observations are used as evidence for counternomic claims. I show that these cases resist being characterized in familiar interventionist lines, and I suggest a characterization of my own.

In a recent paper, Ishtiyaque Haji and Michael McKenna argue that my attack on Frankfurt-style cases fails. I had argued that we cannot be confident that agents in these cases retain their responsibility-underwriting capacities, because what capacities an agent has can depend on features of the world external to her, including merely counterfactual interveners. Haji and McKenna argue that only when an intervention is actual does the agent gain or lose a capacity. Here I demonstrate that this claim is (...) false: capacities are dispositions that can be constituted by features of the environment that are currently inactive. I also reply to two other objections: that my argument begs the question against historical accounts of moral responsibility and that it leaves the very locus of free action, our mental acts, untouched. (shrink)

This paper studies the causal interpretation of counterfactual sentences using a modifiable structural equation model. It is shown that two properties of counterfactuals, namely, composition and effectiveness, are sound and complete relative to this interpretation, when recursive (i.e., feedback-less) models are considered. Composition and effectiveness also hold in Lewis's closest-world semantics, which implies that for recursive models the causal interpretation imposes no restrictions beyond those embodied in Lewis's framework. A third property, called reversibility, holds in nonrecursive causal models but (...) not in Lewis's closest-world semantics, which implies that Lewis's axioms do not capture some properties of systems with feedback. Causal inferences based on counterfactual analysis are exemplified and compared to those based on graphical models. (shrink)

This paper presents a counterfactual account of what a mechanism is. Mechanisms consist of parts, the behavior of which conforms to generalizations that are invariant under interventions, and which are modular in the sense that it is possible in principle to change the behavior of one part independently of the others. Each of these features can be captured by the truth of certain counterfactuals.

We clarify the status of the so-called causal minimality condition in the theory of causal Bayesian networks, which has received much attention in the recent literature on the epistemology of causation. In doing so, we argue that the condition is well motivated in the interventionist (or manipulability) account of causation, assuming the causal Markov condition which is essential to the semantics of causal Bayesian networks. Our argument has two parts. First, we show that the causal minimality condition, rather than an (...) add-on methodological assumption of simplicity, necessarily follows from the substantive interventionist theses, provided that the actual probability distribution is strictly positive. Second, we demonstrate that the causal minimality condition can fail when the actual probability distribution is not positive, as is the case in the presence of deterministic relationships. But we argue that the interventionist account still entails a pragmatic justification of the causal minimality condition. Our argument in the second part exemplifies a general perspective that we think commendable: when evaluating methods for inferring causal structures and their underlying assumptions, it is relevant to consider how the inferred causal structure will be subsequently used for counterfactual reasoning. (shrink)

I argue that classical counterfactual semantics in the style of Stalnaker, Lewis, and Kratzer validates an inference pattern that is disconfirmed in natural language. The solution is to alter the algorithm we use to handle inconsistency in premise sets: rather than checking all maximally consistent fragments of a premise sets, as in Krazter’s semantics, we selectively remove some of the premises. The proposed implementation starts from standard premise semantics and involves a new ‘filtering’ operation that achieves just this removal. (...) The resulting semantics is interestingly related to the semantics for counterfactuals emerging from Judea Pearl’s causal models framework in computer science: in particular, filtering is a possible worlds semantics counterpart of Pearl’s interventions. (shrink)

A few purported counterexamples to the Adams thesis have cropped up in the literature in the last few decades. I propose a theory that accounts for them, in a way that makes the connections between indicative conditionals and counterfactuals clearer.

Ever since Wesley Salmon’s theory, the mechanical approach to causality has found an increasing number of supporters who have developed it in different directions. Mechanical views such as those advanced by Stuart Glennan, Jim Bogen and Peter Machamer, Lindley Darden and Carl Craver have met with broad consensus in recent years. This paper analyses the main features of these mechanical positions and some of the major problems they still face, referring to the latest debate on mechanisms, causal explanation and the (...) relationship between mechanisms and counterfactuals. I shall claim that the mechanical approach can be recognised as having a fundamental explanatory power, whereas the counterfactual approach, recently developed mainly by Jim Woodward and essentially linked to the notion of intervention, has an important heuristic role. Claiming that mechanisms are by no means to be seen as parasitic on counterfactuals or less fundamental than them – as it has been recently suggested –, and that yet counterfactuals can play a part in a conceptual analysis of causation, I shall look for hints in support of the peaceful coexistence of the two. (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)

This paper presents a counterfactual account of what a mechanism is. Mechanisms consist of parts, the behavior of which conforms to generalizations that are invariant under interventions, and which are modular in the sense that it is possible in principle to change the behavior of one part independently of the others. Each of these features can be captured by the truth of certain counterfactuals.

According to Woodward’s influential account of explanation, explanations have a counterfactual structure, and explanatory counterfactuals are analysed in terms of causal relations and interventions. In this paper, we provide a formal semantics of explanatory counterfactuals based on a Ramsey Test semantics of conditionals. Like Woodward’s account, our account is guided by causal considerations. Unlike Woodward’s account, it makes no reference to causal graphs and it also covers cases of explanation where interventions are impossible.

Causal models show promise as a foundation for the semantics of counterfactual sentences. However, current approaches face limitations compared to the alternative similarity theory: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This paper addresses these difficulties using exogenous interventions, where causal interventions change the values of exogenous variables rather than structural equations. This model accommodates judgments about backtracking counterfactuals, extends to logically complex counterfactuals, and validates (...) familiar principles of counterfactual logic. This combines the interventionist intuitions of the causal approach with the logical advantages of the similarity approach. (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)

James Woodward’s manipulationist counterfactual theory of explanation offers strong tools for an adequate approach to explanation endeavours. One of these tools is the notion of intervention, which serves as a guiding principle for identifying explanations as causal, thus preserving the unidirectionality of explanatory praxis. Nevertheless, in this paper, I argue that in some cases of explanation, this notion has a rather redundant role since it is either impossible to define or it can be replaced by other types of manipulations (...) or both. I shall show these shortcomings of intervention in two particularly detailed analyses of an economic example pertaining to Okun’s law and the pendulum explanation. In analysing the former example, I contend that given the complex causal situation of economic phenomena, the notion of intervention is inept at helping explain them. In the latter example, I show that the notion of intervention is readily replaceable by other types of variation—conceptual or mathematical. Finally, I shall end this article by examining the case of pendulum explanation in light of Woodward’s new approach to explanation direction. In the end, the conclusion is that the variables of the pendulum law are in a symmetrical relationship. (shrink)

This paper defends a counterfactual account of explanation, according to which successful explanation requires tracing patterns of counterfactual dependence of a special sort, involving what I call active counterfactuals. Explanations having this feature must appeal to generalizations that are invariant--stable under certain sorts of changes. These ideas are illustrated by examples drawn from physics and econometrics.

Philosophers have long analyzed the truth-condition of counterfactual conditionals in terms of the possible-worlds semantics advanced by Lewis [13] and Stalnaker [23]. In this paper, I argue that, from the perspective of philosophical semantics, the causal modeling semantics proposed by Pearl [17] and others (e.g., Briggs [3]) is more plausible than the Lewis-Stalnaker possible-worlds semantics. I offer two reasons. First, the possible-worlds semantics has suffered from a specific type of counterexamples. While the causal modeling semantics can handle such examples (...) with ease, the only way for the possible-worlds semantics to do so seems to cost it its distinctive status as a philosophical semantics. Second, the causal modeling semantics, but not the possible-worlds semantics, has the resources enough for accounting for both forward-tracking and backtracking counterfactual conditionals. (shrink)

In the artificial intelligence literature a promising approach to counterfactual reasoning is to interpret counterfactual conditionals based on causal models. Different logics of such causal counterfactuals have been developed with respect to different classes of causal models. In this paper I characterize the class of causal models that are Lewisian in the sense that they validate the principles in Lewis’s well-known logic of counterfactuals. I then develop a system sound and complete with respect to this class. The resulting (...) logic is the weakest logic of causal counterfactuals that respects Lewis’s principles, sits in between the logic developed by Galles and Pearl and the logic developed by Halpern, and stands to Galles and Pearl’s logic in the same fashion as Lewis’s stands to Stalnaker’s. (shrink)

“If the tower is any taller than 320 ms, it may collapse,” Eiffel thinks out loud. Although understanding this counterfactual poses no trouble, the most successful interventionist semantics struggle to model it because the antecedent can come about in infinitely many ways. My aim is to provide a semantics that will make modeling such counterfactuals easy for philosophers, computer scientists, and cognitive scientists who work on causation and causal reasoning. I first propose three desiderata that will guide my theory: (...) it should be general, yet conservative, yet useful. Next, I develop a formalization of events in the form of an algebra. I identify an event with all the ways in which it can be brought about and provide rules for determining the referent of an arbitrary event description. I apply this algebra to counterfactuals expressed using underdeterministic causal models-models that encode non-probabilistic causal indeterminacies. Specifically, I develop semaphore interventions, which represent how the target system may be modified from without in a coordinated fashion. This, in turn, allows me to bring about any event within a single model. Finally, I explain the advantages of this semantics over other interventionist competitors. (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)

How do people come to opposite causal judgments about societal problems, such as whether a public health policy reduced COVID‐19 cases? The current research tests an understudied cognitive mechanism in which people may agree about what actually happened (e.g., that a public health policy was implemented and COVID‐19 cases declined), but can be made to disagree about the counterfactual, or what would have happened otherwise (e.g., whether COVID‐19 cases would have declined naturally without intervention) via comparison cases. Across two (...) preregistered studies (total N = 480), participants reasoned about the implementation of a public policy that was followed by an immediate decline in novel virus cases. Study 1 shows that people's judgments about the causal impact of the policy could be pushed in opposite directions by emphasizing comparison cases that imply different counterfactual outcomes. Study 2 finds that people recognize they can use such information to influence others. Specifically, in service of persuading others to support or reject a public health policy, people systematically showed comparison cases implying the counterfactual outcome that aligned with their position. These findings were robust across samples of U.S. college students and politically and socioeconomically diverse U.S. adults. Together, these studies suggest that implied counterfactuals are a powerful tool that individuals can use to manufacture others’ causal judgments and warrant further investigation as a mechanism contributing to belief polarization. (shrink)

The attributable fraction (population) has attracted much attention from a theoretical perspective and has been used extensively to assess the impact of potential health interventions. However, despite its extensive use, there is much confusion about its concept and calculation methods. In this article, we discuss the concepts of and calculation methods for the attributable fraction and related measures in the counterfactual framework, both with and without stratification by covariates. Generally, the attributable fraction is useful when the exposure of (...) interest has a causal effect on the outcome. However, it is important to understand that this statement applies to the exposed group. Although the target population of the attributable fraction (population) is the total population, the causal effect should be present not in the total population but in the exposed group. As related measures, we discuss the preventable fraction and prevented fraction, which are generally useful when the exposure of interest has a preventive effect on the outcome, and we further propose a new measure called the attributed fraction. We also discuss the causal and preventive excess fractions, and provide notes on vaccine efficacy. Finally, we discuss the relations between the aforementioned six measures and six possible patterns using a conceptual schema. (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)

The paper focuses on a recent challenge brought forward against the interventionist approach to the meaning of counterfactual conditionals. According to this objection, interventionism cannot account for the interpretation of right-nested counterfactuals, the problem being its strict interventionism. We will report on the results of an empirical study supporting the objection. Furthermore, we will extend the well-known logic of intervention with a new operator expressing an alternative notion of intervention that does away with strict interventionism. This new notion of (...) intervention operates on the valuation of the variables in a causal model, and not on their functional dependencies. (shrink)

Mere capacity views hold that agents who can intervene in an unfolding movement are performing an agentially controlled action, regardless of whether they do intervene. I introduce a simple argument to show that the noncausal explanation offered by mere capacity views fails to explain both control and action. In cases where bodily subsystems, rather than the agent, generate control over a movement, agents can often intervene to override non-agential control. Yet, contrary to what capacity views suggest, in these cases, this (...) capacity to intervene does not amount to agential control or action. I illustrate this with a case study of how passive breathing, a mere behavior, is misclassified by mere capacity views. I end by revisiting the central alternative to mere capacity views: causal control views. Advances in our understanding of how agents exert control over unfolding movements indicate that the nature of control is characterized by ubiquitous, small-scale causal interventions. (shrink)

Mere capacity views hold that agents who can intervene in an unfolding movement are performing an agentially controlled action, regardless of whether they do intervene. I introduce a simple argument to show that the noncausal explanation offered by mere capacity views fails to explain both control and action. In cases where bodily subsystems, rather than the agent, generate control over a movement, agents can often intervene to override non-agential control. Yet, contrary to what capacity views suggest, in these cases, this (...) capacity to intervene does not amount to agential control or action. I illustrate this with a case study of how passive breathing, a mere behavior, is misclassified by mere capacity views. I end by revisiting the central alternative to mere capacity views: causal control views. Advances in our understanding of how agents exert control over unfolding movements indicate that the nature of control is characterized by ubiquitous, small-scale causal interventions. (shrink)