Causal representations are distinguished from non-causal ones by their ability to predict the results of interventions. This widely-accepted view suggests the following adequacy condition for causal models: a causal model is adequate only if it does not contain variables regarding which it makes systematically false predictions about the results of interventions. Here I argue that this condition should be rejected. For a class of equilibrium systems, there will be two incompatible causal models depending on whether one intervenes upon a certain (...) variable to fix its value, or ‘lets go’ of the variable and allows it to vary. The latter model will fail to predict the result of interventions on the let-go-of variable. I argue that there is no basis for preferring one of these models to the other, and thus that models failing to predict interventions on particular variables can be just as adequate as those making no such false predictions. This undermines a key argument (Dash in Caveats for causal reasoning with equilibrium models. University of Pittsburgh. PhD thesis, 2003) against relying upon causal models inferred from equilibrium data. (shrink)

A common strategy for simplifying complex systems involves partitioning them into subsystems whose behaviors are roughly independent of one another at shorter timescales. Dynamic causal models clarify how doing so reveals a system’s nonequilibrium causal relationships. Here I use these models to elucidate the idealizations and abstractions involved in representing a system at a timescale. The models reveal that key features of causal representations—such as which variables are exogenous—may vary with the timescale at which a system is considered. This has (...) implications for debates regarding which systems can be represented causally. (shrink)

There is traditionally a division of labor between economics and economic history, the task of economic history being that of studying the evolving constraints of economic mechanisms. Recent theorizing on endogenous structural change challenges this view: if structural change is endogenous to economic mechanisms, any strict division of labor between causal analysis and structural analysis breaks down. Rather such views call for an integration of causal and structural analysis. This paper looks for such a methodology, noting especially the contributions made (...) by two Swedish economists, Johan Åkerman (1896-1982) and Erik Dahmén (1916-2005). With evolutionary, Marxian, Hayekian and critical realist contributions, the paper examines notions of endogenous structural change with the aim of expounding how the notion can be thought in terms of underlying mechanisms to aid such structural analysis. The findings of endogenous mechanisms underlying structural change are crystallized into a mathematical model and a simple methodological framework. (shrink)

In Salmon's discussion of his account of statistical relevance and statistical explanation there is a peculiarity in the selection of examples. Where he wishes to show that statistical accounts are reasonably treated as explanatory, he draws examples from the social sciences, such as juvenile delinquency. But when he explains the concept of 'causal' relevance, the examples are selected from the natural sciences. This conceals difficulties with salmon's account of causality in the face of multiple causes such as are characteristic of (...) the social sciences. Salmon's account is shown not to escape difficulties associated with Simon's earlier approach. (shrink)

Selection explanations explain some non-accidental generalizations in virtue of a selection process. Such explanations are not particulaizable - they do not transfer as explanations of the instances of such generalizations. This is unlike many explanations in the physical sciences, where the explanation of the general fact also provides an explanation of its instances (i.e. standard D-N explanations). Are selection explanations (e.g. in biology) therefore a different kind of explanation? I argue that to understand this issue, we need to see that (...) a standard D-N explanation of some non-accidental generalization (al Fs are Gs) may also ipso facto explain its contrapositive (all non-Gs are non-Fs), but the explanation is particularizable with respect to the former but not to the latter. This can be seen by noting that the Raven Paradox counterexample to the H-D model of confirmation also generates a counterexample to the D-N model of explanation (all ravens are black does not explain why the non-black shoe is a non-raven). In such cases it is natural to take the generalization with the positive predicates to have a particularizable explanation. However, this need not be the case, and in selection explanations it is the generalization with the positive predicates whose explanation is no particularizable. Thus there is no need to suppose that selection explanations are fundamentally different. (shrink)

This essay defends two theses that jointly establish a link between causal and explanatory asymmetry. The first thesis is that statements specifying facts about effects, unlike statements specifying facts about causes, are not "independently variable". The second thesis is that independent variability among purportedly explanatory factors is a necessary condition on scientific explanations.

Dynamical models of cognition have played a central role in recent cognitive science. In this paper, we consider a common strategy by which dynamical models describe their target systems neither as purely static nor as purely dynamic, but rather using a hybrid approach. This hybridity reveals how dynamical models involve representational choices that are important for understanding the relationship between dynamical and non-dynamical representations of a system.

Causal representations are distinguished from non-causal ones by their ability to predict the results of interventions. This widely-accepted view suggests the following adequacy condition for causal models: a causal model is adequate only if it does not contain variables regarding which it makes systematically false predictions about the results of interventions. Here I argue that this condition should be rejected. For a class of equilibrium systems, there will be two incompatible causal models depending on whether one intervenes upon a certain (...) variable to fix its value, or ‘lets go’ of the variable and allows it to vary. The latter model will fail to predict the result of interventions on the let-go-of variable. I argue that there is no basis for preferring one of these models to the other, and thus that models failing to predict interventions on particular variables can be just as adequate as those making no such false predictions. This undermines a key argument against relying upon causal models inferred from equilibrium data. (shrink)

According to Kant each person has an empirical character, which is ultimately grounded in one’s free choice. The popular Causal Laws interpretation of empirical character holds that it consists of the causal laws governing our psychology. I argue that this reading has difficulties explaining moral change, the ‘gradual reformation’ of our empirical character: Causal laws cannot change and hence cannot be gradually reformed. I propose an alternative Causal Powers interpretation of empirical character, where our empirical character consists of our mind’s (...) causal powers. The resulting picture of empirical character allows for moral change and Kantian weakness of will. (shrink)

In the foundations of Fechner's psychophysics, the concept of “functional relation” plays a highly relevant role in three different respects: (1) in respect to the principles of measurement, (2) in respect to the mind-body problem, and (3) in respect to the concept of a law of nature. In all three cases, it is important to explain the difference between a functional dependency of a variable upon another and a causal relationship between two (or more) variables. In all three respects, Ernst (...) Mach developed Fechner's ideas further and tried to extend the lessons he had learned from using the concept of a functional relation in psychophysics to the whole of science. For all three cases, I explain why they are still relevant for psychophysics and philosophy today. (shrink)

Skyrms's formulation of the argument against stochastic hidden variables in quantum mechanics using conditionals with chance consequences suffers from an ambiguity in its "conservation" assumption. The strong version, which Skyrms needs, packs in a "no-rapport" assumption in addition to the weaker statement of the "experimental facts." On the positive side, I argue that Skyrms's proof has two unnoted virtues (not shared by previous proofs): (1) it shows that certain difficulties that arise for deterministic hidden variable theories that exploit a nonclassical (...) probability theory extend to the stochastic case; (2) the use of counterfactual conditionals relates the Bell puzzle to Dummett's (1976) discussion of realism in quantum mechanics. (shrink)

There have been two principal paradigms for the formulation of the causal relation--logical implication and functional relationship. In this paper, I present a case for preferring the functional relationship formulation and then discuss how the functional relationship formulation may be implemented in the probabilistic case in a manner analogous to the way others have implemented the logical implication formulation in the probabilistic case. I show how the "local independence" assumption found in many models used in the behavioral and social sciences (...) is a consequence of the functional relation concept of probabilistic causality. I also discuss why causal relations are underdetermined in experience and are to be dealt with pragmatically in a hypothesis-testing framework. (shrink)

Economics prefers complete explanations: general over partial equilibrium, microfoundational over aggregate. Similarly, probabilistic accounts of causation frequently prefer greater detail to less as in typical resolutions of Simpson’s paradox. Strategies of causal refinement equally aim to distinguish direct from indirect causes. Yet, there are countervailing practices in economics. Representative-agent models aim to capture economic motivation but not to reduce the level of aggregation. Small structural vector-autoregression and dynamic stochastic general-equilibrium models are practically preferred to larger ones. The distinction between exogenous (...) and endogenous variables suggests partitioning the world into distinct subsystems. The tension in these practices is addressed within a structural account of causation inspired by the work of Herbert Simon’s, which defines cause with reference to complete systems adapted to deal with incomplete systems and piecemeal evidence. The focus is on understanding the constraints that a structural account of causation places on the freedom to model complex or lower-order systems as simpler or higher-order systems and on to what degree piecemeal evidence can be incorporated into a structural account. (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)

: Among the current philosophical accounts of causation two are the most prominent. The first is James Woodward's interventionist counterfactual approach; the second is the mechanistic approach advocated by Peter Machamer, Lindley Darden, Carl Craver, Jim Bogen and Stuart Glennan. Thecounterfactual 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. In this paper I examine them both in some detail. After pointing out some important problems that both approaches face, I argue that there is a sense in which the counterfactual approach is more basic than the mechanistic one in that a proper account of mechanisms depends on counterfactuals while counterfactuals need not be supported (or depend on) mechanisms. Nonetheless, I also argue that if both approaches work in tandem in practice, they can offer us a better understanding of aspects of Hume's secret connexion and hence a glimpse of it. (shrink)

The purpose of this essay is to defend the deductive-nomological model of explanation against a number of criticisms that have been made of it. It has traditionally been thought that scientific explanations were causal and that scientific explanations involved deduction from laws. In recent years, however, this three-fold identity has been challenged: there are, it is argued, causal explanations that are not scientific, scientific explanations that are not deductive, deductions from laws that are neither causal explanations nor scientific explanations, and (...) causal explanations that involve no deductions from laws. The aim of the present essay is to defend the traditional identities, and to show that the more recent attempts at invalidating them fail in their object. More specifically, this essay argues that a Humean version of the deductive-nomological model of explanation can be defended as the correct account of scientific explanation of individual facts and processes, and as the correct account of causal explanations of individual facts and processes. The deductive-nomological model holds that to explain an event E, say that a is G, one must find some initial conditions C, say that a is F, and a law or theory T such that T and C jointly entail E, and both are essential to the deduction. (shrink)

According to common judicial standard, judgment in favor ofplaintiff should be made if and only if it is more probable than not thatthe defendant''s action was the cause for the plaintiff''s damage (or death). This paper provides formal semantics, based on structural models ofcounterfactuals, for the probability that event x was a necessary orsufficient cause (or both) of another event y. The paper then explicates conditions under which the probability of necessary (or sufficient)causation can be learned from statistical data, and (...) shows how data fromboth experimental and nonexperimental studies can be combined to yieldinformation that neither study alone can provide. Finally, we show thatnecessity and sufficiency are two independent aspects of causation, andthat both should be invoked in the construction of causal explanations for specific scenarios. (shrink)