The discovery of conjunctive causes--factors that act in concert to produce or prevent an effect--has been explained by purely covariational theories. Such theories assume that concomitant variations in observable events directly license causal inferences, without postulating the existence of unobservable causal relations. This article discusses problems with these theories, proposes a causal-power theory that overcomes the problems, and reports empirical evidence favoring the new theory. Unlike earlier models, the new theory derives (a) the conditions under which covariation implies conjunctive causation (...) and (b) functions relating observable events to unobservable conjunctive causal strength. This psychological theory, which concerns simple cases involving 2 binary candidate causes and a binary effect, raises questions about normative statistics for testing causal hypotheses regarding categorical data resulting from discrete variables. (shrink)

This paper focuses on the causal pluralism that emerges from the new mechanistic discussion on causality. The reason for our choice of the New Mechanical Philosophy is twofold. Firstly, the NMP is one of the dominant approaches within the current philosophy of science and has produced very extensive literature on the problem of causation. Secondly, the intersection of the understanding of nature via mechanisms and the focus on explanatory practice across different scientific fields has brought a great renewal of (...) causal explanations within the mechanistic framework. After characterizing the four dominant approaches to causation present in the NMP, we argue that each approach gives an important insight into the philosophical discussion about causality, but at the same time presents certain limitations. The main philosophical issues and their internal problems with respect to each approach are discussed, and in the following sections, we explicate the similarities and differences between those approaches. In the final section, we address the issue of causal pluralism and argue that the new mechanical approach to causation deserves its own place in the debate on causality, since it is an interesting case of interactive pluralism with respect to causality. (shrink)

Existing research has shown that norm violations influence causal judgments, and a number of different models have been developed to explain these effects. One such model, the necessity/sufficiency model, predicts an interac- tion pattern in people’s judgments. Specifically, it predicts that when people are judging the degree to which a particular factor is a cause, there should be an interaction between (a) the degree to which that factor violates a norm and (b) the degree to which another factor in the (...) situation violates norms. A study of moral norms (N = 1000) and norms of proper functioning (N = 3000) revealed robust evidence for the predicted interaction effect. The implications of these patterns for existing theories of causal judgments is discussed. (shrink)

A novel semantic naturalization program is proposed. Its three main differences from informational semantics approaches are as follows. First, it makes use of a perceptually based, four-factor interactive causal relation in place of a simple nomic covariance relation. Second, it does not attempt to globally naturalize all semantic concepts, but instead it appeals to a broadly realist interpretation of natural science, in which the concept of propositional truth is off-limits to naturalization attempts. And third, it treats all semantic concepts as (...) being purely abstract, so that concrete cognitive states are only indexed by them rather than instantiating them. (shrink)

The paper contains a novel realist account of causal production and the necessary connection between cause and effect. I argue that the asymmetric relation between causally connected events must be regarded as a product of a symmetric interaction between two or more entities. All the entities involved contribute to the producing, and so count as parts of the cause, and they all suffer a change, and so count as parts of the effect. Cause and effect, on this account, are two (...) different states of the very same compound substance, or system, one coming to exist out of the other. (shrink)

Recently, a strong hylomorphic reading of Aristotelian emotions has been put forward, one that allegedly eliminates the problem of causal interaction between soul and body. Taking the presentation of emotions in de An. I 1 as a starting point and basic thread, but relying also on the discussion of Rh. II, I will argue that this reading only takes into account two of the four causes of emotions, and that, if all four of them are included into the picture, then (...) a causal interaction of mind and body remains within Aristotelian emotions, independent of how strongly their hylomorphism is understood. Beyond the discussion with this recent reading, the analysis proposed of the fourfold causal structure of emotions is also intended as a hermeneutical starting point for a comprehensive analysis of particular emotions in Aristotle. Through the different causes Aristotle seems to account for many aspects of the complex phenomenon of emotion, including its physiological causes, its mental causes, and its intentional object. (shrink)

In this paper, I propose a framework that supports both free will and anomalous mind-matter interaction (psychokinesis). I begin by considering the argument by the physicist Sean Carroll that the laws of physics as we understand them rule out psychokinesis (and other modes of psi). I find Carroll’s claims problematic, in part due to what I believe are misunderstandings of arguments borrowed from David Hume. I proceed to consider a more dispositional notion of causality (in contrast to one characterized (...) by universal and necessary laws) which I argue is more hospitable to both psychokinesis and free will. I then incorporate recent work from the philosophy of mind and science to arrive at a framework that supports both real volition and psychokinesis, which I argue are intimately linked. This approach is fundamentally dispositional, but grounded in an ontologically prior field of awareness and potentiality. I also consider that the regularities (or causal natures) we observe in our physical world are ultimately supported by what we might term teleological “intentions” within a nonlocal, mind-like quantum ground. (shrink)

It is possible for a causal factor to raise the probability of a second factor in some situations while lowering the probability of the second factor in other situations. Must a genuine cause always raise the probability of a genuine effect of it? When it does not always do so, an "interaction" with some third factor may be the reason. I discuss causal interaction from the perspectives of Giere's counterfactual characterization of probabilistic causal connection (1979, 1980) and the "contextual unanimity" (...) model developed by, among others, Cartwright (1979) and Skyrms (1980). I argue that the contextual unanimity theory must exercise care, in a new way that seems to have gone unnoticed, in order to adequately accommodate the phenomenon, and that the counterfactual theory must be substantially revised; although it will still, pending clarification of a second kind of revision, be unable to accommodate a kind of interaction exemplified in cases like those described by Sober (1982). (shrink)

Signal causality, the prohibition of superluminal information transmission, is the fundamental property shared by quantum measurement theory and relativity, and it is the key to understanding the connection between nonlocal measurement effects and elementary interactions. To prevent those effects from transmitting information between the generating and observing process, they must be induced by the kinds of entangling interactions that constitute measurements, as implied in the Projection Postulate. They must also be nondeterministic as reflected in the Born Probability Rule. The (...) nondeterminism of entanglement-generating processes explains why the relevant types of information cannot be instantiated in elementary systems, and why the sequencing of nonlocal effects is, in principle, unobservable. This perspective suggests a simple hypothesis about nonlocal transfers of amplitude during entangling interactions, which yields straightforward experimental consequences. (shrink)

Wesley Salmon in Scientific Explanation and the Causal Structure of the World offers a causal account of scientific explanation (Salmon 1984). In this context he develops an analysis of the concept of causality, an analysis which marks a break with various contemporary theories of causation including the Neo-Humean view, counterfactual theory, and manipulability theory. His analysis also goes well beyond the confines of probabilistic accounts such as that developed by Suppes. Salmon attempts to explicate the concept of causation by (...) way of two concepts, that of a causal process and that of a causal interaction, which, he defines with some precision and care. In this paper I will present a series of problem cases which run contrary to Salmon’s line of analysis.In developing his analysis of causation Salmon makes use of the notion of a “process”. (shrink)

Concerning any object of philosophical analysis, we can ask several questions, including the two posed in the title of this paper. Despite difficulties in formulating a precise criterion to distinguish causal processes from pseudoprocesses, and causal interactions from mere spatiotemporal intersections, I argue that Salmon answered the first of these questions with extraordinary clarity. The second question, by contrast, has received very little attention. I will present two problems: in the first, it seems that Salmon has provided exactly the conceptual (...) resources needed to solve the problem; in the second, it is difficult to see how causal processes and interactions may be used to shed any light. In general, the way to carry Salmon's program forward will be to demonstrate that these resources can be made to do real philosophical work. (shrink)

According to Spinoza mental events and physical events are identical. What makes Spinoza's identity theory tempting is that it solves the problem of mind body interaction rather elegantly: mental events and physical events can be causally related to each other because mental events are physical events. However, Spinoza seems to deny that there is any causal interaction between mental and physical events. My aim is to show that Spinoza's apparent denial of mind body interaction can be reconciled with the identity (...) theory. I argue that Spinoza had both an extensional and an intensional concept of cause and when Spinoza seems to deny mind body interaction he is having in mind the intensional concept of cause. This intensional concept of cause corresponds to that of causal explanation. I will argue that Spinoza anticipated Donald Davidson's view that even though mental events cannot be explained by referring to physical events and vice versa, mental and physical events are causally related to each other. (shrink)

When a cause interacts with unobserved factors to produce an effect, the contingency between the observed cause and effect cannot be taken at face value to infer causality. Yet, it would be computationally intractable to consider all possible unobserved, interacting factors. Nonetheless, two experiments found that when an unobserved cause is assumed to be fairly stable over time, people can learn about such interactions and adjust their inferences about the causal efficacy of the observed cause. When they observed a (...) period in which a cause and effect were associated followed by a period of the opposite association, rather than concluding a complete lack of causality, subjects inferred an unobserved, interacting cause. The interaction explains why the overall contingency between the cause and effect is low and allows people to still conclude that the cause is efficacious. (shrink)

Using Bayesian network causal models, we provide a simple general account of probabilistic causal interaction. We also detail problems in the leading accounts by Ellery Eells, and any others which require valence reversals, contextual unanimity, or average effects.

Wesley Salmon has developed a theory of causation which makes use of the concepts of a "causal process" and a "causal interaction." Roughly, a causal process is a process which transmits its own structure, and a causal interaction is an intersection of processes which transforms the character of these processes. The cause-effect relation is analyzed as a causal interaction followed by a causal process which terminates in a further causal interaction. In this paper I present a series of problem cases (...) which run "counter" to Salmon's account. (shrink)

Artificial Intelligence (AI) and Philosophy of Science share a fundamental problem—that of understanding causality. Bayesian network techniques have recently been used by Judea Pearl in a new approach to understanding causality and causal processes (Pearl, 2000). Pearl’s approach has great promise, but needs to be supplemented with an explicit account of causal interaction. Thus far, despite considerable interest, philosophy has provided no useful account of causal interaction. Here we provide one, employing the concepts of Bayesian networks. With it (...) we demonstrate the failure of one of philosophy’s more sophisticated attempts to deal with the concept of causal interaction, that of Ellery Eells’ Probabilistic Causality (1991). (shrink)

Artificial Intelligence (AI) and Philosophy of Science share a fundamental problem—understanding causality. Bayesian networks have recently been used by Judea Pearl in a new approach to understanding causality (Pearl, 2000). Part of understanding causality is understanding causal interaction. Bayes nets can represent any degree of causal interaction, and researchers normally try to limit interactions, usually by replacing the full CPT with a noisy-OR function. But we show that noisy-OR and another common model are merely special cases of (...) the general linear systems definition of noninteraction. However, they apply in different situations, and we can measure the degree of causal interaction relative to any such model. (shrink)

My aim in this chapter is to explain how and why all human cognition depends on the body for Leibniz. I will show that there are three types of dependence: (a) the body is needed in order to supply materials, or content, for thinking; (b) the body is needed in order to give us the opportunity for the discovery of innate ideas; and (c) the body is needed in order to provide sensory notions as vehicles of thought. The third type (...) is intimately connected to the faculty of the imagination, which supplies the sensory vehicles of thought. Hence, the paper will also examine Leibniz’s account of that faculty and its operations. Before doing any of this, however, I will address the elephant in the room: Leibniz’s claim that mind and body do not interact at all, metaphysically speaking. The correspondence between mind and body is merely a matter of what he calls ‘pre-established harmony’. At first glance, this Leibnizian doctrine may suggest a radical disembodiment of all human cognition. Yet, I will argue that this is not the case and that, quite to the contrary, pre-established harmony requires that cognition is embodied in one important sense. (shrink)

In this commentary, I first acknowledge points of common ground with the target article by Bruineberg and colleagues. Then, I consider how certain ambiguities could be resolved by considering spatiotemporal constraints on causality. In particular I show how blanket closure emerges from localized interactions between temporally separable subsystems, and how this points to valuable directions of future research. Finally, I close with a process note discussing the allegorical implications of the authors' creative title.

The notion of causal explanation is an essential element of the naturalistic world view. This view is typically interpreted to claim that we are only licensed to postulate entities that make a causal difference , or have causal power . The rest are epiphenomena and hence eliminable from the correct view of reality. The worry that some entities and phenomena that we take for granted mental properties in particular turn out to be epiphenomenal, can be seen as stemming from this (...) sort of naturalistic attitude. This thesis reviews the issue of causal explanation within the context of the naturalistic philosophy of mind. It is argued that there is no single monolithic, unanimously accepted notion of causation that the naturalist should be committed to. Views vary on what this notion amounts to exactly, and fields of science vary with respect to their causal commitments. However, the naturalist can still presume that a scientifically informed philosophical account of causation exists, an account that is fundamentally philosophical, but also sensitive to actual scientific practice and its view of reality. The central issue of the current naturalistic philosophy of mind is the so-called problem of causal exclusion. According to this, the assumption that mental states could have genuine and autonomous effects on the physical world is inconsistent with physical commitments, namely the idea that mental states are necessarily neurally based and the idea that the physical world is causally complete. The causal exclusion argument claims that mental causes must be reduced to physical causes, as there remains no role for independent mental causes. The thesis reviews some central responses to the causal exclusion argument. It is shown that within the context of the interventionist notion of causation, inter-level causation can be ruled out. The causal exclusion argument would thus find support, contrary to what the proponents of the interventionist view typically claim. However, the result is also shown to have the corollary that purely higher-level, mental-to-mental causation is possible. The thesis suggests that this offers a consistent view of mental causation for a naturalist to hold. (shrink)

My target article (henceforth referred to as TA) presents evidence for causal interactions between consciousness and brain and some standard ways of accounting for this evidence in clinical practice and neuropsychological theory. I also point out some of the problems of understanding such causal interactions that are not addressed by standard explanations. Most of the residual problems have to do with how to cross the “explanatory gap” from consciousness to brain. I then list some of the reasons why the route (...) across this gap suggested by physicalism won't work, in spite of its current popularity in consciousness studies. My own suggested route across the explanatory gap is more subterranean, where consciousness and brain can be seen to be dual aspects of a unifying, psychophysical mind. Some of the steps on this deeper route still have to be filled in by empirical research. But (as far as I can judge) there are no gaps that cannot be filled—just a different way of understanding consciousness, mind, brain and their causal interaction, with some interesting consequences for our understanding of free will. The commentaries on TA examined many aspects of my thesis viewed from both Western and Eastern perspectives. This reply focuses on how dual-aspect monism compares with currently popular alternatives such as “nonreductive physicalism”, clarifies my own approach, and reconsiders how well this addresses the “hard” problems of consciousness. We re-examine how conscious experiences relate to their physical/functional correlates and whether useful analogies can be drawn with other, physical relationships that appear to have dual-aspects. We also examine some fundamental differences between Western and Eastern thought about whether the existence of the physical world or the existence of consciousness can be taken for granted (with consequential differences about which of these is “hard” to understand). I then suggest a form of dual-aspect Reflexive Monism that might provide a path between these ancient intellectual traditions that is consistent with science and with common sense. (shrink)

In our commentary we ask whether we should ultimately endeavour to find the deep causes of behaviours? Then we discuss two extensions of the proposed framework: (1) Mendelian randomisation and (2) hypothesis-free gene–environment interaction (leveraging heterogeneity in genetic associations). These complementary methods help move us towards second-generation causal knowledge, ultimately understanding mechanistic pathways and identifying more effective intervention targets.

A symmetric monoidal category naturally arises as the mathematical structure that organizes physical systems, processes, and composition thereof, both sequentially and in parallel. This structure admits a purely graphical calculus. This paper is concerned with the encoding of a fixed causal structure within a symmetric monoidal category: causal dependencies will correspond to topological connectedness in the graphical language. We show that correlations, either classical or quantum, force terminality of the tensor unit. We also show that well-definedness of the concept of (...) a global state forces the monoidal product to be only partially defined, which in turn results in a relativistic covariance theorem. Except for these assumptions, at no stage do we assume anything more than purely compositional symmetric-monoidal categorical structure. We cast these two structural results in terms of a mathematical entity, which we call a causal category . We provide methods of constructing causal categories, and we study the consequences of these methods for the general framework of categorical quantum mechanics. (shrink)

b>: Replacing faulty nineteenth century physics by its orthodox quantum successor converts the earlier materialist conception of nature to a structure that does not enforce the principle of the causal closure of the physical. The quantum laws possess causal gaps, and these gaps are filled in actual scientific practice by inputs from our streams of consciousness. The form of the quantum laws permits and suggests the existence of an underlying reality that is built not on substances, but on psychophysical events, (...) and on objective tendencies for these events to occur. These events constitute intrinsic mind-brain connections. They are fundamental links between brain processes described in physical terms and events in our streams of consciousness. This quantum ontology confers upon our conscious intentions the causal efficacy assigned to them in actual scientific practice, and creates a substance- free interactive dualism. This putative quantum ontology has previously been shown to have impressive explanatory power in both psychology and neuroscience. Here it is used to reconcile the existence of physically efficacious conscious free will with causal anomalies of both the Libet and Einstein-Rosen-Podolsky types. (shrink)

It is time that we in organization sciencesdevelop and implement a new mental model forcause and effect relationships. The dominantmodel in research dates at least to the 1700sand no longer serves the full purposes of thesocial science research problems of the21st century. Traditionally, research is``essentially concerned with two-variableproblems, linear causal trains, one cause andone effect, or with few variables at the most''(von Bertalanffy, 1968, p. 12). However, theliterature is replete with examples ofphenomena in which the traditional cause andeffect construct does (...) not allow for greaterunderstanding and insight into the phenomena. Different conceptions of cause and effectrelationships have been developed includingproducer/product relationships (Ackoff 1981),design causality (Argyris and Schon, 1996), andfour classes of causal models (Schwartz andOgilvy, 1979). Of interest here is thepossibility of mutual causality, ``theassumption that the relationship between two(or more) phenomena is heavily influenced bythe presence of feedback loops that areinstantaneous, or nearly so'' (Dent, 1999). Maturana's (1998, Maturana and Varela, 1987)work on a new epistemology and ontologyprovides a foundation for the alternative modelof cause and effect proposed here. Thisinteraction model includes the dynamics of thetraditional X and Y, but adds the structure ofX (A), the structure of Y (B), the environment(E), and time (T). (shrink)

Leibniz accepts causal independence, the claim that no created substance can causally interact with any other. And Leibniz needs causal independence to be true, since his well-known pre-established harmony is premised upon it. So, what is Leibniz’s argument for causal independence? Sometimes he claims that causal interaction between substances is superfluous. Sometimes he claims that it would require the transfer of accidents, and that this is impossible. But when Leibniz finds himself under sustained pressure to defend causal independence, those are (...) not the reasons that he marshals in its defense. Instead, deep into his long correspondence with Burchard de Volder, he gives a different sort of argument, one that has gone nearly unnoticed by commentators and has not yet been properly understood. In part, this is because the argument develops slowly over four years of correspondence. It emerges in early 1704, but it is formulated tersely and appears murky unless understood in light of Leibniz and De Volder’s tangled exchanges. There Leibniz argues that, on his distinctive ontology of an infinity of created substances, no two created substances could possibly causally interact, for roughly the same reasons that some Cartesians like De Volder deny interaction between minds and bodies on their substance dualist ontology. In this paper I draw out this lost argument, explain it and the metaphysics on which Leibniz builds it, and untangle Leibniz and De Volder’s exchanges concerning causation from which this argument results. (shrink)

In this paper I put forward a suggestion for identifying causality in micro-systems with the specific quantum field theoretic interactions that occur in such systems. I first argue — along the lines of general transference theories — that such a physicalistic account is essential to an understanding of causation; I then proceed to sketch the concept of interaction as it occurs in quantum field theory and I do so from both a formal and an informal point of view. Finally, (...) I present reasons for thinking that only a quantum field theoretic account can do the job — in particular I rely on a theorem by D. Currie and to the effect that interaction cannot be described in (a Hamiltonian formulation of) Classical Mechanics. Throughout the paper I attempt to suggest that the widespread scepticism about the ability of quantum theory to support a theory of causality is mistaken and rests on several misunderstandings. (shrink)

The problem of structure and agency has been the subject of intense debate in the social sciences for over 100 years. This book offers a solution. Using a critical realist version of the theory of emergence, Dave Elder-Vass argues that, instead of ascribing causal significance to an abstract notion of social structure or a monolithic concept of society, we must recognise that it is specific groups of people that have social structural power. Some of these groups are entities with emergent (...) causal powers, distinct from those of human individuals. Yet these powers also depend on the contributions of human individuals, and this book examines the mechanisms through which interactions between human individuals generate the causal powers of some types of social structures. The Causal Power of Social Structures makes particularly important contributions to the theory of human agency and to our understanding of normative institutions. (shrink)

Although there has been much recent discussion on mechanisms in philosophy of science and social theory, no shared understanding of the crucial concept itself has emerged. In this paper, a distinction between two core concepts of mechanism is made on the basis that the concepts correspond to two different research strategies: the concept of mechanism as a componential causal system is associated with the heuristic of functional decomposition and spatial localization and the concept of mechanism as an abstract form of (...) interaction is associated with the strategy of abstraction and simple models. The causal facts assumed and the theoretical consequences entailed by an explanation with a given mechanism differ according to which concept of mechanism is in use. Research strategies associated with mechanism concepts also involve characteristic biases that should be taken into account when using them, especially in new areas of application. (shrink)

Cognitive Science, Volume 46, Issue 1, January 2022.

In this important book, Ellery Eells explores and refines philosophical conceptions of probabilistic causality. In a probabilistic theory of causation, causes increase the probability of their effects rather than necessitate their effects in the ways traditional deterministic theories have specified. Philosophical interest in this subject arises from attempts to understand population sciences as well as indeterminism in physics. Taking into account issues involving spurious correlation, probabilistic causal interaction, disjunctive causal factors, and temporal ideas, Professor Eells advances the analysis of (...) what it is for one factor to be a positive causal factor for another. A salient feature of the book is a theory of token level probabilistic causation in which the evolution of the probability of a later event from an earlier event is central. This will be a book of crucial significance to philosophers of science and metaphysicians; it will also prove stimulating to many economists, psychologists, and physicists. (shrink)

We affirm the utility of integrative modeling, according to which it is advantageous to move beyond “one-at-a-time binary paradigms” through studies that position themselves within realistic multidimensional design spaces. We extend the integrative modeling approach to a target domain with which we are familiar, the consequences of bilingualism on mind and brain, often referred to as the “bilingual advantage.” In doing so, we highlight work from our group consistent with integrative modeling.

Real causal systems are complicated. Despite this, causal learning research has traditionally emphasized how causal relations can be induced on the basis of idealized events, i.e. those that have been mapped to binary variables and abstracted from time. For example, participants may be asked to assess the efficacy of a headache-relief pill on the basis of multiple patients who take the pill (or not) and find their headache relieved (or not). In contrast, the current study examines learning via interactions with (...) continuous dynamic systems, systems that include continuous variables that interact over time (and that can be continuously observed in real time by the learner). To explore such systems, we develop a new framework that represents a causal system as a network of stationary Gauss--Markov (``\OU'') processes and show how such \textit{OU networks} can express complex dynamic phenomena such as feedback loops and oscillations. To assess adult's abilities to learn such systems, we conducted an experiment in which participants were asked to identify the causal relationships of a number of OU networks, potentially carrying out multiple, temporally-extended interventions. We compared their judgments to a normative model for learning OU networks as well as a range of alternative and heuristic learning models from the literature. We found that, although participants exhibited substantial learning of such systems, they committed certain systematic errors. These successes and failures were best accounted for by a model that describes people as focusing on pairs of variables, rather than evaluating the evidence with respect to the full space of possible structural models. We argue that our approach provides both a principled framework for exploring the space of dynamic learning environments as well as new algorithmic insights into how people interact successfully with a continuous causal world. (shrink)

This article suggests a revision of the theory of causal nets. In section 1 we introduce an axiomatization of TCN based on a realistic understanding. It is shown that the causal Markov condition entails three independent principles. In section 2 we analyze indeterministic decay as the major counterexample to one of these principles: screening off by common causes. We call SCC-violating common causes interactive causes. In section 3 we develop a revised version of TCN, called TCN*, which accounts for interactive (...) causes. It is shown that there are interactive causal models that admit of no faithful noninteractive reconstruction. (shrink)

I propose an argument for the thesis that laws of nature are necessary in the sense of holding in all worlds sharing the properties of the actual world, on the basis of a principle I propose to call the Causal Criterion of Reality . The CCR says: for an entity to be real it is necessary and sufficient that it is capable to make a difference to causal interactions. The crucial idea here is that the capacity to interact causally - (...) or to contribute to determining causal interactions - is not only the ultimate metaphysical ground for the existence of an entity, but it also provides a criterion for determining the nature of that entity, i.e. its properties. The alternative is to conceive of laws of nature as contingent: they could be different from what they are like in the actual world, where that possibility is understood to be metaphysical, not only epistemic. For the sake of this paper, I shall accept Armstrong's thesis that laws of nature are relations between universals. I also follow Armstrong in the view that both the existence and the properties of particulars are metaphysically independent of the existence and identity of other particular. However, what is controversial and what I shall challenge is his thesis that universals are like particulars in the following respect: according to Armstrong, each universal is a logically distinct entity whose existence and identity is independent of the existence and identity of other universals. My aim in this paper is to show that the identity of a universal is entirely determined by its lawful relations to other universals. The crucial premise I use is the thesis that the CCR is a universal criterion, which applies both to particulars and universals. From the thesis that the identity of a universal is exclusively determined by laws, it follows that laws are necessary in the sense that they cannot differ without the universals they link also being different. This creates a difficulty for those authors who, as Armstrong, accept the CCR but nevertheless defend the view that laws are contingent. (shrink)

One of Stuart Glennan's most prominent contributions to the new mechanist debate consists in his reductive analysis of higher-level causation in terms of mechanisms (Glennan, 1996). In this paper I employ the causal Bayes net framework to reconstruct his analysis. This allows for specifying general assumptions which have to be satis ed to get Glennan's approach working. I show that once these assumptions are in place, they imply (against the background of the causal Bayes net machinery) that higher-level causation indeed (...) reduces to interactions between component parts of mechanisms. I also briefly discuss the plausibility of these assumptions and some consequences for the mechanism debate. (shrink)

Many powers metaphysicians postulate both active and passive powers, understood as distinct kinds of intrinsic causal properties of objects. I argue that the category of passive power is superfluous. I also offer a diagnosis of how philosophers are misled to postulate passive powers.

This chapter contains sections titled: Causation and Dualism Why Not Locate Souls in Space? Property/Event Dualism or Dual Aspect Theory.

The article analyses recent English publications in Cartesian studies that deal with two problems: the problem of the intrinsic coherence of Descartes’s doctrine of the real distinction and interaction between mind and body and the problem of the consistency of this doctrine with the causal principle formulated in the Third Meditation. The principle at issue is alternatively interpreted by different Cartesian scholars either as the Hierarchy Principle, that the cause should be at least as perfect as its effects, or the (...) Containment Principle, that the cause should contain all there is in its effects. The author argues that Descartes’s claim that it is inconceivable how things of different natures can interact does not conflict with the acknowledgement of interaction between things of different natures in the case of soul and body. The case is made that Cartesian mind-body interaction can agree with both the Hierarchy Principle and the Containment Principle, because the Principle is about total and efficient cause, whereas in the interaction, mental and brain states are only partial causes. In particular, in the case of the causality in the brain-to-mind direction, the mind is conditioned by brain states to form the corresponding specific ideas on the basis of its innate general ideas of movements, forms, colours, etc. Eventually, for Descartes, the most natural way to deal with worries about the possibility of mind-brain interaction is to rely on God’s omnipotence, which certainly enables Him to arrange for such interaction. (shrink)

The development of causal modelling since the 1950s has been accompanied by a number of controversies, the most striking of which concerns the Markov condition. Reichenbach's conjunctive forks did satisfy the Markov condition, while Salmon's interactive forks did not. Subsequently some experts in the field have argued that adequate causal models should always satisfy the Markov condition, while others have claimed that non-Markovian causal models are needed in some cases. This paper argues for the second position by considering the multi-causal (...) forks, which are widespread in contemporary medicine (Section 2). A non-Markovian causal model for such forks is introduced and shown to be mathematically tractable (Sections 6, 7, and 8). The paper also gives a general discussion of the controversy about the Markov condition (Section 1), and of the related controversy about probabilistic causality (Sections 3, 4, and 5). (shrink)

THE ART OF CAUSAL CONJECTURE Glenn Shafer Table of Contents Chapter 1. Introduction........................................................................................ ...........1 1.1. Probability Trees..........................................................................................3 1.2. Many Observers, Many Stances, Many Natures..........................................8 1.3. Causal Relations as Relations in Nature’s Tree...........................................9 1.4. Evidence............................................................................................ ...........13 1.5. Measuring the Average Effect of a Cause....................................................17 1.6. Causal Diagrams..........................................................................................20 1.7. Humean Events............................................................................................23 1.8. Three Levels of Causal Language................................................................27 1.9. An Outline of the Book................................................................................27 Chapter 2. Event Trees............................................................................................... .....31 2.1. Situations and Events...................................................................................32 2.2. The Ordering of Situations and Moivrean Events.......................................35 2.3. Cuts................................................................................................ ..............39 2.4. Humean Events............................................................................................43 2.5. (...) Moivrean Variables......................................................................................49 2.6. Humean Variables........................................................................................53 2.7. Event Trees for Stochastic Processes...........................................................54 2.8. Timing in Event Trees.................................................................................56 2.9. Intersecting Event Trees...............................................................................60 2.10. Notes on the Literature...............................................................................61 Chapter 3. Probability Trees...........................................................................................63 3.1. Some Types of Probability Trees.................................................................64 ii 3.2. Axioms for the Probabilities of Moivrean Events.......................................68 3.3. Zero Probabilities....................................................................................... ..70 3.4. A Sample-Space Analysis of the Event-Tree Axioms.................................72 3.5. Probabilities and Expected Values for Variables.........................................74 3.6. Martingales......................................................................................... .........79 3.7. The Expectation of a Variable in a Cut........................................................83 3.8. Conditional Expected Value and Conditional Expectation.........................87 Chapter 4. The Meaning of Probability...........................................................................91 4.1. The Interpretation of Expected Value..........................................................92 4.2. The Interpretation of Expectation................................................................95 4.3. The Long Run..............................................................................................98 4.4. Changes in Belief.........................................................................................101 4.5. The Empirical Validation of Probability......................................................106 4.6. The Diversity of Uses of Probability...........................................................108 4.7. Notes on the Literature.................................................................................110 Chapter 5. Independent Events.......................................................................................113 5.1. Independence........................................................................................ .......114 5.2. Weak Independence.....................................................................................118 5.3. The Principle of the Common Cause...........................................................121 5.4. Conditional Independence............................................................................128 5.5. Notes on the Literature.................................................................................133 Chapter 6. Events Tracking Events.................................................................................135 6.1. Tracking............................................................................................ ...........137 6.2. Tracking and Conditional Independence.....................................................142 6.3. Stochastic Subsequence...............................................................................143 6.4. Singular Diagrams for Stochastic Subsequence...........................................147 6.5. Conjunctive and Interactive Forks...............................................................149 Chapter 7. Events as Signs of Events..............................................................................153 iii 7.1. Sign................................................................................................ ..............154 7.2. Weak Sign................................................................................................ ....159 7.3. The Ethics of Causal Talk............................................................................160 7.4. Screening Off...............................................................................................16 2 Chapter 8. Independent Variables...................................................................................167 8.1. Unconditional Independence........................................................................170 8.2. Conditional Independence............................................................................175 8.3. Independence for Partitions.........................................................................177 8.4. Independence for Families of Variables......................................................182 8.5. Individual Properties of the Independence Relations...................................186 Chapter 9. Variables Tracking Variables........................................................................189 9.1. Tracking and Conditional Independence: A Summary...............................190 9.2. Strong Tracking............................................................................................ 192 9.3. Strong Tracking and Conditional Independence..........................................198 9.4. Stochastic Subsequence...............................................................................201 9.5. Functional Dependence................................................................................203 9.6. Tracking in Mean.........................................................................................204 9.7. Linear Tracking............................................................................................ 207 9.8. Tracking by Partitions..................................................................................210 9.9. Tracking by Families of Variables...............................................................212 Chapter 10. Variables as Signs of Variables...................................................................215 10.1. Sign................................................................................................ ............219 10.2. Linear Sign................................................................................................ .222 10.3. Scored Sign................................................................................................ 225 10.4. Families of Variables.................................................................................227 Chapter 11. AnTheory of Event Trees.............................................................229 11.1. Event Trees as Sets of Sets........................................................................230 11.2. Event Trees as Partially Ordered Sets........................................................232 iv 11.3. Regular Event Trees...................................................................................240 11.4. The Resolution of Moivrean Variables......................................................244 11.5. Humean Events and Variables...................................................................246 Chapter 12. Martingale Trees..........................................................................................247 12.1. Examples of Decision Trees......................................................................249 12.2. The Meaning of Probability in a Decision Tree.........................................253 12.3. Martingales......................................................................................... .......257 12.4. The Structure of Martingale Trees.............................................................261 12.5. Probability and Causality...........................................................................265 12.6. Lower and Upper Probability.....................................................................269 12.7. The Law of Large Numbers.......................................................................272 12.8. Notes on the Literature...............................................................................274 Chapter 13. Refining............................................................................................ ...........275 13.1. Examples of Refinement............................................................................277 13.2. A Constructive Definition of Finite Refinement.......................................281 13.3. Axioms for Refinement..............................................................................282 13.4. Lifting Moivrean Events and Variables.....................................................288 13.5. Refining Martingale Trees.........................................................................288 13.6. Grounding........................................................................................... .......294 Chapter 14. Principles of Causal Conjecture..................................................................299 14.1. The Diversity of Causal Explanation.........................................................302 14.2. The Mean Effect of the Happening of a Moivrean Event..........................305 14.3. The Effect of a Humean Variable..............................................................311 14.4. Attribution and Generality.........................................................................316 14.5. The Statistical Measurement of the Effect of a Cause...............................319 14.6. Measurement by Experiment.....................................................................320 14.7. Using Our Knowledge of How Things Work............................................322 v 14.8. Sampling Error...........................................................................................329 14.9. The Sampling Frame..................................................................................329 14.10. Notes on the Literature.............................................................................330 Chapter 15. Causal Models.............................................................................................3 31 15.1. The Causal Interpretation of Statistical Prediction....................................333 15.2. Generalizing to a Family of Exogenous Variables....................................337 15.3 Some Joint Causal Diagrams......................................................................339 15.4. Causal Path Diagrams................................................................................342 15.5. Causal Relevance Diagrams.......................................................................346 15.6. The Meaning of Latent Variables..............................................................352 15.7. Notes on the Literature...............................................................................357 Chapter 16. Representing Probability Trees...................................................................359 16.1. Three Graphical Representations...............................................................361 16.2. Skeletal Simplifications.............................................................................368 16.3. Martingale Trees in Type Theory...............................................................371 Appendix A. Huygens’s Probability Trees.....................................................................379 Huygens’s Manuscript in Translation..................................................................380 Appendix B. Some Elements of Graph Theory..............................................................385 B1. Undirected Graphs........................................................................................385 B2. Directed Graphs............................................................................................38 6 Appendix C. Some Elements of Order Theory...............................................................393 C1. Partial and Quasi Orderings.........................................................................393 C2. Singular and Joint Diagrams for Binary Relations.......................................394 C3. Lattices............................................................................................ .............395 C4. The Lattice of Partitions of a Set..................................................................396 Appendix D. The Sample-Space Framework for Probability.........................................399 D1. Probability Measures....................................................................................399 D2. Variables........................................................................................... ...........400 vi D3. Families of Variables...................................................................................401 D4. Expected Value............................................................................................402 D5. The Law of Large Numbers.........................................................................405 D6. Conditional Probability................................................................................406 D7. Conditional Expected Value........................................................................407 Appendix E. Prediction in Probability Spaces................................................................409 E1. Conditional Distribution...............................................................................411 E2. Regression on a Single Variable...................................................................412 E3. Regression on a Partition or a Family of Variables......................................415 E4. Linear Regression on a Single Variable.......................................................418 E5. Linear Regression on a Family of Variables................................................422 Appendix F. Sample-Space Concepts of Independence.................................................425 F1. Overview............................................................................................ ...........426 F2. Independence Proper.....................................................................................432 F3. Unpredictability in Mean..............................................................................434 F4. Simple Uncorrelatedness..............................................................................437 F5. Mixed Uncorrelatedness...............................................................................438 F6. Partial Uncorrelatedness...............................................................................440 F7. Independence for Partitions..........................................................................442 F8. Independence for Families of Variables.......................................................445 F9. The Basic Role of Uncorrelatedness.............................................................448 F10. Dawid’s Axioms.........................................................................................449 Appendix G. Prediction Diagrams..................................................................................453 G1. Path Diagrams............................................................................................ ..454 G2. Generalized Path Diagrams..........................................................................462 G3. Relevance Diagrams.....................................................................................466 G4. Bubbled Relevance Diagrams......................................................................475 Appendix H. Abstract Stochastic Processes...................................................................479 vii H1. Probability Conditionals and Probability Distributions...............................477 H2. Abstract Stochastic Processes......................................................................479 H3. Embedding Variables and Processes in a Sample Space.............................480 References.......................................................................................... ..............................491. (shrink)

Causal Dispositionalism provides an account of causation based on an ontology of causal powers, properties with causal essence. According to the account, causation can be analysed in terms of the interaction of powers and its subsequent production of their effect. Recently, Baltimore, J. A. has raised a challenge against two competing approaches, the compositional view and the mutual manifestation view, to explain what makes powers interactive – the interaction gap. In this paper, we raise the challenge of explaining what makes (...) powers productive – the production gap. While Baltimore’s verdict is tentatively favouring, we find both approaches wanting. Our conclusion is that Causal Dispositionalists should take Baltimore’s and our critique seriously. Powers cannot cause their effects just by bearing the name “causal”. To deserve their names, more metaphysical details are needed. (shrink)

Mental causation, idea that it is us – via our minds – who cause bodily actions is as commonsensical as it is indispensable for our understanding of ourselves as rational agents. Somewhat less uncontroversial, but nonetheless widespread (at least among ordinary people) is the idea that the mind is non-physical, following the intuition that what is physical can neither act nor think nor judge morally. Taken together, and cast into a metaphysical thesis, the two intuitions yield interactive dualism: the view (...) that human persons and their minds are non-physical but can nonetheless interact with their bodies, most notably through their brains. This thesis has two main objectives: first, to defend interactive dualism against objections, and second, to show how it can blend in with a physical world in which laws of nature hold. The first part (chapter 1) consists in a brief motivation of interactive dualism as opposed to non-interactive dualism. I argue that non-interactive, epiphenomenalist dualism sacrifices so many crucial aspects of our human existence that interactive dualism is highly to be preferred to it, barring even stronger counterarguments against the latter. It is those putative counterarguments I address in parts II and III. Part II takes on the philosophical objections from the causal closure of the physical and from causal heterogeneity. The former takes the success of physics and physiology as basis for the doctrine of the causal closure of the physical (CCP). I argue against this that there is not only no convincing argument for CCP, but also that any science-based belief in CCP cannot be epistemically justified. As regards the causal heterogeneity objection, it is ‘weighed and found wanting’, because it relies on unwarranted assumptions about causation. In part III, I examine the objection from energy conservation. It roughly says that if interactive dualism were true, then energy would not be conserved, which physics taught us cannot be the case. My reply is that the underlying conception of energy (and momentum) conservation is wrong-headed and not the one that actual physicists use. Instead of being categorical and global, conservation laws are conditional and local, thereby making natural room for mental interaction. Some dualists, however, have sought to make interactive dualism conservation-friendly, notably by invoking quantum physics; I show that these attempts are unnecessary and create more problems than they solve. Finally, in chapter 9, I turn the tables on non-interactionists by investigating current neurophysiological literature on volitional actions, which, though not addressing the question directly, still encourages the interactive dualist picture more than a non-interactionist one. Part IV is about the interplay between interactive dualism and the laws of nature. The ultimate goal is, if possible, to come up with a theory of the laws of nature that explains the lawlike behavior of nature and at the same time makes room for interaction. I begin by pointing out that the laws of nature are or at least should be what physicalists worry about (chapter 10). I then proceed with a historical survey on the development of the notion of laws of nature (chapter 11) that sheds light on its theistic origin. This is followed by an inquiry into the question how a law of nature could possibly be broken (chapter 12) and a survey of the extant metaphysical theories of the laws of nature with special regard to their receptivity to interaction (chapter 13). Finally, in chapter 14 I develop a theory of the laws of nature both faithful to their divine origin and the possibility of mental interaction: dispositionalist divine decretalism, a synthesis of dispositionalism and Jeffrey Koperski’s divine decretalism. (shrink)