Causation Without Influence

David Lewis’s latest theory of causation defines the causal link in terms of the relation of influence between events. It turns out, however, that one event’s influencing another is neither a necessary nor sufficient condition for its being a cause of that event. In the article one particular case of causality without influence is presented and developed. This case not only serves as a counterexample to Lewis’s influence theory, but also threatens earlier counterfactual analyses of causation by admitting a particularly troublesome type of preemption. The conclusion of the article is that Lewis’s influence method of solving the preemption problem fails, and that we need a new and fresh approach to the cases of redundant causation if we want to hold on to the counterfactual analysis of causation.     

Reversing the Counterfactual Analysis of Causation

The counterfactual analysis of causation has focused on one particular counterfactual conditional, taking as its starting‐point the suggestion that C causes E iff (～C □→ ～E). In this paper, some consequences are explored of reversing this counterfactual, and developing an account starting with the idea that C causes E iff (～E □→ ～C). This suggestion is discussed in relation to the problem of pre‐emption. It is found that the ‘reversed’ counterfactual analysis can handle even the most difficult cases of pre‐emption with only minimal complications. The paper closes with a discussion of the wider philosophical implications of developing a reversed counterfactual analysis, especially concerning the differentiation of causes from causal conditions, causation by absences, and the extent to which causes suffice for their effects.     

When Possibility Informs Reality: Counterfactual Thinking as a Cue to Causality

People often engage in counterfactual thinking, that is, imagining alternatives to the real world and mentally playing out the consequences. Yet the counterfactuals people tend to imagine are a small subset of those that could possibly be imagined. There is some debate as to the relation between counterfactual thinking and causal beliefs. Some researchers argue that counterfactual thinking is the key to causal judgments; current research suggests, however, that the relation is rather complex. When people think about counterfactuals, they focus on ways to prevent bad or uncommon outcomes; when people think about causes, they focus on things that covary with outcomes. Counterfactual thinking may affect causality judgments by changing beliefs about the probabilities of possible alternatives to what actually happened, thereby changing beliefs as to whether a cause and effect actually covary. The way in which counterfactual thinking affects causal attributions may have practical consequences for mental health and the legal system.     

Children’s causal reasoning: Counterfactual thinking occurs for ‘negative’ outcomes only

Harris, German and Mills (Children’s use of counterfactual thinking in causal reasoning. Cognition, 61 (1996), 223–259), following Mackie, argue that children make explicit use of counterfactual thinking in arriving at causal judgments. They showed that children as young as 3, in explaining simple mishap events, made reference to courses of action that a protagonist had rejected, when that course of action would have prevented the observed outcome. It is hypothesized here that such counterfactual thinking might have been invoked by the ‘negative’ mishaps rather than as part of the causal reasoning process. Although the generation of counterfactuals in explanation was replicated using mishap outcomes such as those used by Harris et al., counterfactual thinking was not evident in children’s explanations of ‘positive’ outcomes. These results undermine the view that a counterfactual thinking process, as indexed by reference to possible actions rejected by a protagonist, is necessary for causal reasoning. Alternative characterizations of the relationship between causals and counterfactuals are discussed.     

AGAINST LEWIS's NEW THEORY OF CAUSATION: A STORY WITH THREE MORALS

Abstract: A recent paper by David Lewis, “Causation as Influence”, provides a new theory of causation. This paper presents an argument against the theory, using a series of counterexamples that are, I think, of independent interest to philosophers of causation. I argue that (a) the relation asserted by a claim of the form “C was a cause of E” is distinct from the relation of causal influence, (b) the former relation depends very much, contra Lewis, on the individuation conditions for the event E, and (c) Lewis's account is unsatisfactory as an analysis of either kind of relation. The counterexamples presented in this paper provide, I suggest, some insight into the reasons for the failure of counterfactual accounts of causal relations.     

Causal Explanation and Explanatory Selection

It is observed that in ordinary everyday causal explanations often just one causal factor is mentioned. One causal factor carries the explanatory burden, even if there are several causal factors that are responsible for the event to be explained. This paper deals with the question of how to account for this explanatory selection. I argue for a pragmatic stance towards explanation, that we must attend to the question–answer situation as a whole and the context of the explanation. The context of an explanation includes the inquirer's and the explainer's beliefs and presuppositions relevant for the explanation-seeking question, and these are encoded in a reference class. Furthermore I argue that the explanation-giving answer contains an implicit counterfactual claim, the explanation-giving counterfactual. The solution to the problem of explanatory selection is to be found in the presuppositions encoded by the reference class and the eg-counterfactual. In short we select as explanatory that factor which, together with the presupposition encoded in the reference class we believe will make the eg-counterfactual true. This revised version was published online in June 2006 with corrections to the Cover Date.     

There aren't plenty more fish in the sea: A causal network approach

The current research investigated how lay representations of the causes of an environmental problem may underlie individuals' reasoning about the issue. Naïve participants completed an experiment that involved two main tasks. The causal diagram task required participants to depict the causal relations between a set of factors related to overfishing and to estimate the strength of these relations. The counterfactual task required participants to judge the effect of counterfactual suppositions based on the diagrammed factors. We explored two major questions: (1) what is the relation between individual causal models and counterfactual judgments? Consistent with previous findings (e.g., Green et al., 1998, Br. J. Soc. Psychology, 37, 415), these judgments were best explained by a combination of the strength of both direct and indirect causal paths. (2) To what extent do people use two‐way causal thinking when reasoning about an environmental problem? In contrast to previous research (e.g., White, 2008, Appl. Cogn. Psychology, 22, 559), analyses based on individual causal networks revealed the presence of numerous feedback loops. The studies support the value of analysing individual causal models in contrast to consensual representations. Theoretical and practical implications are discussed in relation to causal reasoning as well as environmental psychology.     

Natural Selection: A Case for the Counterfactual Approach

This paper investigates the conception of causation required in order to make sense of natural selection as a causal explanation of changes in traits or allele frequencies. It claims that under a counterfactual account of causation, natural selection is constituted by the causal relevance of traits and alleles to the variation in traits and alleles frequencies. The “statisticalist” view of selection (Walsh, Matthen, Ariew, Lewens) has shown that natural selection is not a cause superadded to the causal interactions between individual organisms. It also claimed that the only causation at work is those aggregated individual interactions, natural selection being only predictive and explanatory, but it is implicitly committed to a process-view of causation. I formulate a counterfactual construal of the causal statements underlying selectionist explanations, and show that they hold because of the reference they make to ecological reliable factors. Considering case studies, I argue that this counterfactual view of causal relevance proper to natural selection captures more salient features of evolutionary explanations than the statisticalist view, and especially makes sense of the difference between selection and drift. I eventually establish equivalence between causal relevance of traits and natural selection itself as a cause.     

Causes and Counterparts

It follows from David Lewis's counterpart-theoretic analysis of modality and his counterfactual theory of causation that causal claims are relativized to a set of counterpart relations. Call this Shlewis's view. I show how Shlewis's view can provide attractively unified solutions to similar modal and causal puzzles. I then argue that Shlewis's view is better motivated, by his own lights, than the view Lewis actually held, and also better motivated than a similar approach which relativizes causal claims to sets of ‘contrast events’.     

Counterfactual reasoning and the problem of selecting antecedent scenarios

A recent group of social scientists have argued that counterfactual questions play an essential role in their disciplines, and that it is possible to have rigorous methods to investigate them. Unfortunately, there has been little (if any) interaction between these social scientists and the philosophers who have long held that rigorous counterfactual reasoning is possible. In this paper, I hope to encourage some fresh thinking on both sides by creating new connections between them. I describe what I term “problem of selecting antecedent scenarios,” and show that this is an essential challenge in real-life counterfactual reasoning. Then, I demonstrate that the major extant theories of counterfactuals (especially the Lewis/Stalnaker theory and Igal Kvart’s rival account) are unable to solve this problem. I show that there are instances of real-life counterfactual reasoning in the social sciences that are counterexamples to both of these accounts. And finally, I develop a new theory of how to select antecedent scenarios that overcomes these difficulties, and so would be part of a more adequate theory of counterfactuals (and counterfactual reasoning).     

Mechanistic explanation at the limit

Resurgent interest in both mechanistic and counterfactual theories of explanation has led to a fair amount of discussion regarding the relative merits of these two approaches. James Woodward is currently the pre-eminent counterfactual theorist, and he criticizes the mechanists on the following grounds: Unless mechanists about explanation invoke counterfactuals, they cannot make sense of claims about causal interactions between mechanism parts or of causal explanations put forward absent knowledge of productive mechanisms. He claims that these shortfalls can be offset if mechanists will just borrow key tenets of his counterfactual theory of causal claims. What mechanists must bear in mind, however, is that by pursuing this course they risk both the assimilation of the mechanistic theories of explanation into Woodward’s own favored counterfactual theory, and they risk the marginalization of mechanistic explanations to a proper subset of all explanations. An outcome more favorable to mechanists might be had by pursuing an actualist-mechanist theory of the contents of causal claims. While it may not seem obvious at first blush that such an approach is workable, even in principle, recent empirical research into causal perception, causal belief, and mechanical reasoning provides some grounds for optimism.     

Effect of counterfactual and factual thinking on causal judgements

The significance of counterfactual thinking in the causal judgement process has been emphasized for nearly two decades, yet no previous research has directly compared the relative effect of thinking counterfactually versus factually on causal judgement. Three experiments examined this comparison by manipulating the task frame used to focus participants' thinking about a target event. Prior to making judgements about causality, preventability, blame, and control, participants were directed to think about a target actor either in counterfactual terms (what the actor could have done to change the outcome) or in factual terms (what the actor had done that led to the outcome). In each experiment, the effect of counterfactual thinking did not differ reliably from the effect of factual thinking on causal judgement. Implications for research on causal judgement and mental representation are discussed.     

The collider principle in causal reasoning: why the Monty Hall dilemma is so hard

The authors tested the thesis that people find the Monty Hall dilemma (MHD) hard because they fail to understand the implications of its causal structure, a collider structure in which 2 independent causal factors influence a single outcome. In 4 experiments, participants performed better in versions of the MHD involving competition, which emphasizes causality. This manipulation resulted in more correct responses to questions about the process in the MHD and a counterfactual that changed its causal structure. Correct responses to these questions were associated with solving the MHD regardless of condition. In addition, training on the collider principle transferred to a standard version of the MHD. The MHD taps a deeper question: When is knowing about one thing informative about another?     

Causes,Counterfactuals, and Non-Locality

In order to motivate the thesis that there is no single concept of causation that can do justice to all of our core intuitions concerning that concept, Ned Hall has argued that there is a conflict between a counterfactual criterion of causation and the condition of causal locality. In this paper I critically examine Hall's argument within the context of a more general discussion of the role of locality constraints in a causal conception of the world. I present two strategies that defenders of counterfactual accounts of causation can pursue to respond to Hall's challenge—including the adoption of a counterfactual condition that is sufficient for causal action-at-a-distance in place of Hall's ‘process’ condition—and conclude that Hall's argument against counterfactual accounts of causation is unsuccessful.     

Counterfactuals, Autonomy and Downward Causation: Reply to Zhong

In recent papers, Lei Zhong argues that the autonomy solution to the causal exclusion problem is unavailable to anyone that endorses the counterfactual model of causation. The linchpin of his argument is that the counterfactual theory entails the downward causation principle, which conflicts with the autonomy solution. In this note I argue that the counterfactual theory does not entail the downward causation principle, so it is possible to advocate for the autonomy solution to the causal exclusion problem from within the counterfactual theory of causation.     

Probabilistic Causation in Branching Time

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

Children's counterfactual inferences about long and short causal chains

Recent findings on counterfactual reasoning in children have led to the claim that children's developing capacities in the domain of ‘theory of mind’ might reflect the emergence of the ability to engage in counterfactual thinking over the preschool period (e.g. Riggs, Peterson, Robinson & Mitchell, 1998 ). In the study reported here, groups of 3- and 4-year old children were presented with stories describing causal chains of several events, and asked counterfactual thinking tasks involving changes to different points in the chain. The ability to draw successful counterfactual inferences depended strongly on the inferential length of the problem, and the age of the children; while 3-year-olds performed above chance on short inference counterfactuals, they performed below chance on problems involving longer inference chains. Four-year-old children were above chance on all problems. Moreover, it was found that while success on longer chain inference problems was significantly correlated with the ability to pass tests of standard false belief, there was no such relationship for short inference problems, which were significantly easier than false belief problems. These results are discussed in terms of the developmental relationships between causal knowledge, counterfactual thinking and calculating the contents of mental states.     

Short article conformity to the power PC theory of causal induction depends on the type of probe question

P. W. Cheng's () power PC theory of causal induction proposes that causal estimates are based on the power (P) of a potential cause, where P is the contingency between the cause and effect normalized by the base rate of the effect. Most previous research using a standard causal probe question has failed to support the predictions of the power PC model but recently Buehner, Cheng, and Clifford () found that participants responded in terms of causal power when probed with a counterfactual test question, which they argued prompted participants to consider the base rate of the effect. However, Buehner et al. framed their counterfactual question in terms of frequency, a factor that has been demonstrated to decrease base rate neglect in judgements under uncertainty. In the experiment reported here, we sought to disentangle the influence of counterfactual and frequency framing of the probe question to determine which factor is responsible for encouraging responses in terms of causal power.     

Co-responsibility and Causal Involvement

In discussions of moral responsibility for collectively produced effects, it is not uncommon to assume that we have to abandon the view that causal involvement is a necessary condition for individual co-responsibility. In general, considerations of cases where there is “a mismatch between the wrong a group commits and the apparent causal contributions for which we can hold individuals responsible” motivate this move. According to Brian Lawson, “solving this problem requires an approach that deemphasizes the importance of causal contributions”. Christopher Kutz’s theory of complicitious accountability in Complicity from 2000 is probably the most well-known approach of that kind. Standard examples are supposed to illustrate mismatches of three different kinds: an agent may be morally co-responsible for an event to a high degree even if her causal contribution to that event is a) very small, b) imperceptible, or c) non-existent (in overdetermination cases). From such examples, Kutz and others conclude that principles of complicitious accountability cannot include a condition of causal involvement. In the present paper, I defend the causal involvement condition for co-responsibility. These are my lines of argument: First, overdetermination cases can be accommodated within a theory of coresponsibility without giving up the causality condition. Kutz and others oversimplify the relation between counterfactual dependence and causation, and they overlook the possibility that causal relations other than marginal contribution could be morally relevant. Second, harmful effects are sometimes overdetermined by non-collective sets of acts. Over-farming, or the greenhouse effect, might be cases of that kind. In such cases, there need not be any formal organization, any unifying intentions, or any other noncausal criterion of membership available. If we give up the causal condition for coresponsibility it will be impossible to delimit the morally relevant set of acts related to those harms. Since we sometimes find it fair to blame people for such harms, we must question the argument from overdetermination. Third, although problems about imperceptible effects or aggregation of very small effects are morally important, e.g. when we consider degrees of blameworthiness or epistemic limitations in reasoning about how to assign responsibility for specific harms, they are irrelevant to the issue of whether causal involvement is necessary for complicity. Fourth, the costs of rejecting the causality condition for complicity are high. Causation is an explicit and essential element in most doctrines of legal liability and it is central in common sense views of moral responsibility. Giving up this condition could have radical and unwanted consequences for legal security and predictability. However, it is not only for pragmatic reasons and because it is a default position that we should require stronger arguments (than conflicting intuitions about “mismatches”) before giving up the causality condition. An essential element in holding someone to account for an event is the assumption that her actions and intentions are part of the explanation of why that event occurred. If we give up that element, it is difficult to see which important function responsibility assignments could have.