Process Theories of Causation

In this dissertation, I propose a reductive account of causation. This account may be stated as follows: -/- Causation:c is a cause of e within a possibility horizon H iff a) c is process-connected to e, and b) e security-depends on c within H. -/- More precisely, my suggestion is that there are two kinds of causal relata: instantaneous events (defined in Chapter 4) and possibility horizons (defined in Chapter 5). Causation is a ternary relation between two actual instantaneous events (...) – the cause c and the effect e – and a possibility horizon H. -/- I argue that causation has a dual nature: on the one hand, a cause must be connected to its effect via a genuine process; on the other hand, a cause must make a difference to its effect. The first condition – namely, the condition of process-connection (defined in Chapter 6) – captures the sense in which a cause must be connected to its effect via a genuine process. This condition allows my account to separate causation from mere correlation, distinguish genuine causes from preempted backups, and capture how a cause must be at the right level of detail relative to its effect (Chapter 7). -/- The second condition – namely, the condition of security-dependence (defined in Chapter 8) – captures the sense in which a cause must make a difference to its effect. This condition allows my account to yield intuitively correct verdicts on the counterexamples to the transitivity and intrinsicness of causation, resolve the problem of profligate omissions, accommodate structurally isomorphic but causally different cases, and handle contrastive causal claims (Chapter 9 and 10). -/- Finally, my proposed account of causation logically entails restricted versions of three important principles of causal reasoning concerning the sufficiency of counterfactual dependence for causation, and the transitivity and intrinsicness of causation (Chapter 11). (shrink)

In this paper I will argue that what makes our ordinary judgements about token causation (‘actual causation’) true can be explicated in terms of interferences into quasi-inertial processes. These interferences and quasi-inertial processes can in turn be fully explicated in scientific terms. In this sense the account presented here is reductive. I will furthermore argue that this version of a process-theory of causation can deal with the traditional problems that process theories have to face, such as the problem of misconnection (...) and the problem of disconnection (Dowe 2009) as well as with a problem concerning the mis-classification of pre-emption cases (Paul and Hall 2013). (shrink)

This work explains how different theories of causation confront causal overdetermination. Chapters clarify the problem of overdetermination and explore its fundamental aspects. It is argued that a theory of causation can account for our intuitions in overdetermination cases only by accepting that the adequacy of our claims about causation depends on the context in which they are evaluated.The author proposes arguments for causal contextualism and provides insight which is valuable for resolution of the problem. -/- These chapters enable readers to (...) quickly absorb different perspectives on overdetermination and important theories of causation, therefore it is a work that will have a broad appeal. (shrink)

It has been argued that the use of Big Data in scientific research casts doubt on the need for causal knowledge in making sound predictions. In this article I argue that it is possible to search for productive causes in Big Data if one adopts the 'information-transfer account of causation', a version of the causal process theory. As it stands, the current formulation is inadequate as it does not specify how information is to be measured. I consider three concepts of (...) information: information as knowledge update, information as entropy and information as algorithmic complexity, and argue that the last of these provides the best way to achieve this with respect to Big Data. How this can be used to search for causal connections among Big Data is then illustrated with respect to exposomics research. (shrink)

I provide a theory of causation formulated within the causal modeling framework. This theory is model-invariant in the following sense: if the theory says that C caused (didn't cause) E in a causal model, M, then it will continue to say that C caused (didn't cause) E once we've removed an inessential variable from M. On this theory, we can understand causation as a model-invariant generalization of a relation of causal production. Begin by saying that C produces E iff they (...) are linked by an uninterrupted process propagating non-inertial states. Given this definition, whether we say that C produces E will depend upon whether we include or remove inessential variables lying along, or feeding into, the path from C to E. Weakening production so as to make the relation model-invariant delivers causation. (shrink)

Causal overdetermination occupies an uncomfortable place within all the major theories of causation. A natural solution to the problems it gives rise to would be to resolve overdetermination into preemption or joint causation. However, such a solution would seem to lead to individuate events in a fragile manner. The issue of such modal fragility is addressed and it is argued that events designated as effects are always fragile in a natural way and the putative problems of adopting modal fragility can (...) be resolved. It is also studied whether causal overdetermination could work in a setting where the overdetermining causes occur in different levels of reality. Typically postulated higher-level causes are shown to contradict the definitional features of overdetermination. There is thus no reasonable role for causal overdetermination and the notion should be abandoned. (shrink)

Evidence for unconscious semantic representation suggests that a cognitive unconscious exists. Phenomenal consciousness cannot easily be shown to deal with complex cognitive operations such as those involved in language translation and creativity. A self-organising phenomenal consciousness that controls brain functions also runs into mind/body problems (well recognised in the consciousness studies literature) that Perruchet & Vinter must address.

Opening Remarks of the Chairman at “Wesley C. Salmon, 1925–2001”: A Symposium Honoring his Contributions to the Philosophy of Science.

This paper investigates the differences between two conceptions of causation which are claimed to amount for causation in indeterministic situations. Recent analyses of indeterministic causation have been based upon mark transmission, and upon probability relations. Both types of analyses were proposed by Reichenbach, who claimed that they were extensionally equivalent. I demonstrate that they are not equivalent, and discuss some implications of this for models of scientific explanation.

This paper details the ontological and epistemic character of activties that occur in mechanisms. It explains why they are sufficient to handle the problems of causation.

After introducing a range of mechanistic theories of causality and some of the problems they face, I argue that while there is a decisive case against a purely mechanistic analysis, a viable theory of causality must incorporate mechanisms as an ingredient. I describe one way of providing an analysis of causality which reaps the rewards of the mechanistic approach without succumbing to its pitfalls.

Part I of this paper introduces a range of mechanistic theories of causality, including process theories and the complex-systems theories, and some of the problems they face. Part II argues that while there is a decisive case against a purely mechanistic analysis, a viable theory of causality must incorporate mechanisms as an ingredient, and describes one way of providing an analysis of causality which reaps the rewards of the mechanistic approach without succumbing to its pitfalls.

: 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)

It is natural to think of causes as difference-makers. What exact difference causes make, however, is an open question. In this paper, I argue that the right way of understanding difference-making is in terms of causal processes: causes make a difference to a causal process that leads to the effect. I will show that this way of understanding difference-making nicely captures the distinction between causing an outcome and helping determine how the outcome happens and, thus, explains why causation is not (...) transitive. Moreover, the theory handles tricky cases that are problematic for competing accounts of difference-making. (shrink)

This paper articulates an account of causation as a collection of information-theoretic relationships between patterns instantiated in the causal nexus. I draw on Dennett’s account of real patterns to characterize potential causal relata as patterns with specific identification criteria and noise tolerance levels, and actual causal relata as those patterns instantiated at some spatiotemporal location in the rich causal nexus as originally developed by Salmon. I develop a representation framework using phase space to precisely characterize causal relata, including their degree (...) of counterfactual robustness, causal profiles, causal connectivity, and privileged grain size. By doing so, I show how the philosophical notion of causation can be rendered in a format that is amenable for direct application of mathematical techniques from information theory such that the resulting informational measures are causal informational measures. This account provides a metaphysics of causation that supports interventionist semantics and causal modeling and discovery techniques. (shrink)

The need to find an intrinsic characterization of what makes a relation between events causal arises not only in local theories of causation like Salmon's process theory but also in global approaches like Lewis' counterfactual theory. According to the localist intuition, whether a process connecting two events is causal should depend only on what goes on between the events, not on conditions that hold elsewhere in the world. If such intrinsic characterizations could be found, an identification of the causal relation (...) in the actual world (though not in other possible worlds) with physical processes may be feasible (the a posteriori identification). I consider recent proposals made for intrinsic characterizations of causality and conclude that none of them is able to deliver the intended result. (shrink)

Many theorists take causality to be the heart of scientific explanation. If we couple this view with the idea, again common enough, that causes are something like sufficient conditions for their effects, then we have raised a difficulty for any theory of scientific explanation that admits "final" statistical explanations, for a statistical explanation must allow that factors relevant to an event to be explained not determine that event, but only make it probable to some degree . If we do not (...) wish to give up the possibility of genuine statistical explanations we must either give up this central causal intuition in explanation, or admit that causes act as something other than sufficient conditions--say in a probabilistic manner. This is, of course, little help unless we can clearly explicate the notion of a probabilistic cause, one of the tasks I have attempted to accomplish in this thesis. ;The general structure of this effort is in three parts. In the first chapter I generate the basic concepts underlying the notion of a probabilistic cause. In this I follow closely the development of this idea as constructed by Wesley Salmon. Within this view scientific explanation consists in the tracing of causal influence via causes as processes, and providing the probability relations among events which are the product of causal interactions. In the second chapter I further develop the idea of causal processes by comparing this notion with the more traditional analyses of causation, the regularity and counterfactual theories. There I show how the process ontology very naturally overcomes problems in both of these views. I also note the advantages this conception of causes holds over these received views with respect to the problem of determinism. In the third and final chapter I discuss, and attempt to overcome, difficulties that are specific to the notion of a probabilistic causal process. (shrink)

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)

Since the late nineties there has been an increased interested in probabilistic logic learning, an area within AI that combines machine learning with logic-based knowledge representation and uncertainty reasoning. Several different formalisms for combining first-order logic with probability reasoning have been proposed, and it has been studied how models in these formalisms can be automatically learned from data. -/- This talk starts with a brief introduction to probabilistic logic learning, after which we will focus on a relatively new formalism known (...) as CP-logic. CP-logic stands for “causal probabilistic logic”. It is a knowledge representation formalism that allows us to write down rules that indicate that a certain combination of conditions may cause certain effects with a particular probability (e.g., tossing a coin may cause a result of heads or tails, each with 50% probability). Besides the fact that this formalism is interesting for knowledge representation in itself, it also offers interesting opportunities from the machine learning point of view. Indeed, given the semantics of these CP-logic programs, learning them from data amounts to extracting probabilistic causal influences from data. We will discuss recent research on learning CP-logic programs, including: algorithms for learning them; how they relate to graphical models; and applications of learning CP-logic programs. (shrink)

The aim of this article is to analyze the main contributions of Wesley C. Salmon to the philosophy of science, that is, his concepts of causation, common cause, and theoretical explanation, and to provide a critique of them. This critique will be based on a comparison of Salmon’s concepts with categories developed by Hegel in his Science of Logic and which can be applied to issues treated by Salmon by means of the above given three concepts. It is the author’s (...) contention that by means of Hegelian categories it becomes possible to provide a critique of Salmon’s philosophy of science and at the same time to enlarge the concept framework of philosophy of science. (shrink)

What does "nature" mean? This general question, central to the social construction of nature, is addressed here by examining one of nature's particulars, Pacific salmon, and by looking at how one group of people, salmon biologists, imbue the fish with meaning. Based upon historical, comparative, and qualitative data, it appears that nature is socially constructed through both cognitive and physical processes. "Salmon"- and indirectly nature - emerges not as a monolithic, timeless, certain entity, but rather as one that is manipulable, (...) fleeting, and the product of a variety of social relations. In particular, public policy and economics appear to have profoundly influenced salmon biologists' cognitive and physical constructions of salmon. (shrink)

Max Kistler’s first book, based on his Paris Ph.D. thesis, is an elaborate defence of a transference theory of causation. Such a theory conceives of causality as the transfer of a conserved quantity. A transference theory of causation is thus one form that a regularity account of causation, as opposed to a counterfactual account, might take. Kistler’s original contribution consists (a) in the way in which he develops an account of causation based on transference and (b) in relating a theory (...) of causation to a specific view of natural laws. Kistler first considers what a relation of causation is and thereby contrasts the transference theory with other explanations (Ch. 1). He then develops a view of natural laws (Ch. 2) and combines this view with his transference theory of causation (Chs. 3 & 4). The second part of the book focuses on causally efficacious properties. Kistler employs the notion of properties that are responsable for a relation of causation. The function of such properties in laws of causation is examined (Chs. 5 & 6). The last chapter discusses examples that are to show how this theory of causation works (Ch. 7). Kistler argues for a realistic view: there is causation in the world independently of whether and how people conceptualize causal relations. The relata of a causal relation are events. An event is the content of a continuous space–time region (which may be as small as being pointlike) (18, 64–68). Events are in space–time what objects are in space (196): An object has spatial parts, whereas an event has spatio–temporal parts. To give an example, a volcano has spatial parts such as a top, whereas an eruption of a volcano can be gentle and limited to its northern side first and then become violent and extending to all sides. There is a relation of causation between two events if and only if at least one conserved quantity is transferred between them (39–40, 100). That is to say: an event x has a certain value of a physical quantity, and that individual value is transferred to another event y.. (shrink)

In this paper, I examine the comparatively neglected intuition of production regarding causality. I begin by examining the weaknesses of current production accounts of causality. I then distinguish between giving a good production account of causality and a good account of production. I argue that an account of production is needed to make sense of vital practices in causal inference. Finally, I offer an information transmission account of production based on John Collier’s work that solves the primary weaknesses of current (...) production accounts: applicability and absences. (shrink)