In this paper, I argue that Spinoza's claim at E1P15 that “Whatever is, is in God, and nothing can be or be conceived without God” remains exegetically troubling. Briefly noting some unresolved difficulties with the two dominant interpretations of Spinoza's account of the relationship between finite modes and God (these being the inherence and causal dependence readings), I move to claim that there is a third, neglected reading available which deserves consideration. I argue that, perhaps surprisingly, Althusser's notion of “structural (...) causality,” putatively derived from Spinoza himself, can be used to construct this reading. Althusser's original notion of “structural causality” is explained and clarified further, its advantages outlined over competing readings of Spinoza, and exegetical evidence for its applicability to Spinoza is then produced. (shrink)

This article illustrates Sheila Dow's notion of ‘structured pluralism’ drawing on a recent empirical body of literature in which multiple research, or ‘Q-Squared’, approaches to causal analysis of poverty analysis have been used in the Global South. It maintains that understanding linguistic differences between schools of thought is quite integral to methodologically-aware critique and to improved methodological practice. The various strands in the Q2 literature together provide a case for methodological pluralism based on claims that knowledge is partial, empirical adjudication (...) is imperfect, the world is complex and combining multiple research approaches adds value. The goals of understanding and explanation are best served if research questions dictate choice of methodological approach rather than the other way around. (shrink)

Causal set theory and the theory of linear structures (which has recently been developed by Tim Maudlin as an alternative to standard topology) share some of their main motivations. In view of that, I raise and answer the question how these two theories are related to each other and to standard topology. I show that causal set theory can be embedded into Maudlin’s more general framework and I characterise what Maudlin’s topological concepts boil down to when applied to discrete linear (...) structures that correspond to causal sets. Moreover, I show that all topological aspects of causal sets that can be described in Maudlin’s theory can also be described in the framework of standard topology. Finally, I discuss why these results are relevant for evaluating Maudlin’s theory. The value of this theory depends crucially on whether it is true that (a) its conceptual framework is as expressive as that of standard topology when it comes to describing well-known continuous as well as discrete models of spacetime and (b) it is even more expressive or fruitful when it comes to analysing topological aspects of discrete structures that are intended as models of spacetime. On the one hand, my theorems support (a): the theory is rich enough to incorporate causal set theory and its definitions of topological notions yield a plausible outcome in the case of causal sets. On the other hand, the results undermine (b): standard topology, too, has the conceptual resources to capture those topological aspects of causal sets that are analysable within Maudlin’s framework. This fact poses a challenge for the proponents of Maudlin’s theory to prove it fruitful. (shrink)

This book is a puzzle inspired by the fresh, colourful rolling landscapes of Gruyère, where a wise man once asked me, “Does the castle over the mountain exist if nobody sees it?” “Yes,” I answered, “as we have all learned that what we know is not always what reality is.” We agree that theoretical ‘knowledge’ needs practical confirmation within reality. But what if we find reality in its very essence, intelligence or knowledge, as Neoplatonists did? The unknown would become unreal, (...) void. When investigating the existence of a cause of all things—the One origin—Damascius, the last head of Plato’s Academy (which closed in 529), writes about its complete ineffability: its lack of position and absence in relation to reality, whether intelligible or not. This void is further reversed into a theoretical framework that defines causality as a global, systemic condition of the world as a whole.About the author: Alexandru Pelin is a philosopher and cyber security professional. (shrink)

Causal reductionism is the widespread assumption that there is no room for additional causes once we have accounted for all elementary mechanisms within a system. Due to its intuitive appeal, causal reductionism is prevalent in neuroscience: once all neurons have been caused to fire or not to fire, it seems that causally there is nothing left to be accounted for. Here, we argue that these reductionist intuitions are based on an implicit, unexamined notion of causation that conflates causation with prediction. (...) By means of a simple model organism, we demonstrate that causal reductionism cannot provide a complete and coherent account of ‘what caused what’. To that end, we outline an explicit, operational approach to analyzing causal structures. (shrink)

Carey argues that the aspects of categorization that are diagnostic of deep conceptual structure and, by extension, narrow conceptual content, must be distinguished from those aspects that are incidental to categorization tasks. For natural kind concepts, discriminating between these two types of processes is complicated by the role of explanatory stance and the causal history of features in determining category structure.

Recent work has established a framework for explaining the origin of cognitive novelties—qualitatively distinct cognitive traits—in human beings. This niche construction approach argues that humans engineer epistemic environments in ways that facilitate the ontogenetic and phylogenetic development of such novelties. I here argue that attention to the organized relations between content-carrying informational vehicles, or informational form, is key to a valuable explanatory strategy within this project, what I call structural-causal explanations. Drawing on recent work from Cecilia Heyes, and developing a (...) case study around a novel mathematical capacity, I demonstrate how structural-causal explanations can contribute to the niche construction approach by underwriting the application of explanatory tools and generating new empirical targets. (shrink)

More often than not, recently popular structuralist interpretations of physical theories leave the central concept of a structure insufficiently precisified. The incipient causal sets approach to quantum gravity offers a paradigmatic case of a physical theory predestined to be interpreted in structuralist terms. It is shown how employing structuralism lends itself to a natural interpretation of the physical meaning of causal set theory. Conversely, the conceptually exceptionally clear case of causal sets is used as a foil to illustrate how a (...) mathematically informed rigorous conceptualization of structure serves to identify structures in physical theories. Furthermore, a number of technical issues infesting structuralist interpretations of physical theories such as difficulties with grounding the identity of the places of highly symmetrical physical structures in their relational profile and what may resolve these difficulties can be vividly illustrated with causal sets. (shrink)

Even if one can experiment on relevant factors, learning the causal structure of a dynamical system can be quite difficult if the relevant measurement processes occur at a much slower sampling rate than the “true” underlying dynamics. This problem is exacerbated if the degree of mismatch is unknown. This paper gives a formal characterization of this learning problem, and then provides two sets of results. First, we prove a set of theorems characterizing how causal structures change under undersampling. Second, we (...) develop an algorithm for inferring aspects of the causal structure at the “true” timescale from the causal structure learned from the undersampled data. Research on causal learning in dynamical contexts has largely ignored the challenges of undersampling, but this paper provides a framework and foundation for learning causal structure from this type of complex time series data. (shrink)

Le présent livre est audacieux et marque, à n’en pas douter, une avancée pour les recherches sur la pensée de Damascius. Tiré de la thèse de doctorat de l’auteur, le livre interroge le concept de causalité comme procession dans le Traité des premiers principes (De principiis) de Damascius. Pelin entend se distancier de ce qu’il estime être une tendance dominante des études damascéennes (p. 32-33) : plutôt que de lire le De principiis dans une perspective épistémologique et d’y voir une (...) enquêt... (shrink)

Clark Glymour, Richard Scheines, Peter Spirtes and Kevin Kelly. Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.

Recent arguments concerning the nature of causation in evolutionary theory, now often known as the debate between the 'causalist' and 'statisticalist' positions, have involved answers to a variety of independent questions – definitions of key evolutionary concepts like natural selection, fitness, and genetic drift; causation in multi-level systems; or the nature of evolutionary explanations, among others. This Element offers a way to disentangle one set of these questions surrounding the causal structure of natural selection. Doing so allows us to clearly (...) reconstruct the approach that some of these major competing interpretations of evolutionary theory have to this causal structure, highlighting particular features of philosophical interest within each. Further, those features concern problems not exclusive to the philosophy of biology. Connections between them and, in two case studies, contemporary metaphysics and philosophy of physics demonstrate the potential value of broader collaboration in the understanding of evolution. (shrink)

Causal structure learning algorithms have focused on learning in ”batch-mode”: i.e., when a full dataset is presented. In many domains, however, it is important to learn in an online fashion from sequential or ordered data, whether because of memory storage constraints or because of potential changes in the underlying causal structure over the course of learning. In this paper, we present TDSL, a novel causal structure learning algorithm that processes data sequentially. This algorithm can track changes in the generating causal (...) structure or parameters, and requires significantly less memory in realistic settings. We show by simulation that the algorithm performs comparably to batch-mode learning when the causal structure is stationary, and significantly better in non-stationary environments. (shrink)

This dissertation defends the view that concepts encode causal information and, for the first time, applies this view to a range of topics in the philosophy of language and social philosophy. In my first chapter (“Cognitive Essentialism and the Structure of Concepts”), I survey the current empirical and theoretical literature on causal-essentialist theories of concepts. In my second chapter (“Meaning Externalism and Causal Model Theory”), I propose an account of natural kind concepts according to which they encode statistical information of (...) features of a natural kind, and represents these features as causally related to each other. I show that this internalist model of concepts correctly predicts intuitions about Putnam’s twin earth scenario and Kripke’s conceivability cases that historically motivated philosophers of language to accept externalist accounts of meaning. The defended theory of concepts also informs topics that go beyond traditional issues in philosophy of language. In my third chapter (“An Essentialist Theory of the Meaning of Slurs”), I defend the view that slurs are, too, a species of kind terms: slur concepts encode mini-theories which represent an essence-like element that is causally connected to a set of negatively-valenced stereotypical features of a social group. This explains both the peculiar linguistic behavior of slurs and slurs’ dehumanizing effects. In my fourth chapter, I build on this insight, showing that the explicit language in and around pornography depicts women as ‘kinds’ or ‘breeds’ that are naturally made to enjoy certain sexual acts, and argue that this deterministic picture of women dehumanizes them. (shrink)

Research on human causal learning has largely focused on strength learning, or on computational-level theories; there are few formal algorithmic models of how people learn causal structure from covariations. We introduce a model that learns causal structure in a local manner via prediction-error learning. This local learning is then integrated dynamically into a unified representation of causal structure. The model uses computationally plausible approximations of rational learning, and so represents a hybrid between the associationist and rational paradigms in causal learning (...) research. We conclude by showing that the model provides a good fit to data from a previous experiment. (shrink)

Recent debates over the nature of causation, casual inference, and the uses of causal models in counterfactual analysis, involving inter alia Nancy Cartwright (Hunting Causes and Using Them), James Woodward (Making Things Happen), and Judea Pearl (Causation), hinge on how causality is represented in models. Economists’ indigenous approach to causal representation goes back to the work of Herbert Simon with the Cowles Commission in the early 1950s. The paper explicates a scheme for the representation of causal structure, inspired by (...) Simon, and shows how this representation sheds light on some important debates in the philosophy of causation. This structural account is compared to Woodward’s manipulability account. It is used to evaluate the recent debates – particularly, with respect to the nature of causal structure, the identity of causes, causal independence, and modularity. Special attention is given to modeling issues that arise in empirical economics. (shrink)

This paper centers around the notion that internal, mental representations are grounded in structural similarity, i.e., that they are so-called S-representations. We show how S-representations may be causally relevant and argue that they are distinct from mere detectors. First, using the neomechanist theory of explanation and the interventionist account of causal relevance, we provide a precise interpretation of the claim that in S-representations, structural similarity serves as a “fuel of success”, i.e., a relation that is exploitable for the representation using (...) system. Then, we discuss crucial differences between S-representations and indicators or detectors, showing that—contrary to claims made in the literature—there is an important theoretical distinction to be drawn between the two. (shrink)

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)

The Lotka–Volterra predator-prey-model is a widely known example of model-based science. Here we reexamine Vito Volterra’s and Umberto D’Ancona’s original publications on the model, and in particular their methodological reflections. On this basis we develop several ideas pertaining to the philosophical debate on the scientific practice of modeling. First, we show that Volterra and D’Ancona chose modeling because the problem in hand could not be approached by more direct methods such as causal inference. This suggests a philosophically insightful motivation for (...) choosing the strategy of modeling. Second, we show that the development of the model follows a trajectory from a “how possibly” to a “how actually” model. We discuss how and to what extent Volterra and D’Ancona were able to advance their model along that trajectory. It turns out they were unable to establish that their model was fully applicable to any system. Third, we consider another instance of model-based science: Darwin’s model of the origin and distribution of coral atolls in the Pacific Ocean. Darwin argued more successfully that his model faithfully represents the causal structure of the target system, and hence that it is a “how actually” model. (shrink)

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

This paper deals with causal analysis in the social sciences. We first present a conceptual framework according to which causal analysis is based on a rationale of variation and invariance, and not only on regularity. We then develop a formal framework for causal analysis by means of structural modelling. Within this framework we approach causality in terms of exogeneity in a structural conditional model based which is based on (i) congruence with background knowledge, (ii) invariance under a large variety (...) of environmental changes, and (iii) model fit. We also tackle the issue of confounding and show how latent confounders can play havoc with exogeneity. This framework avoids making untestable metaphysical claims about causal relations and yet remains useful for cognitive and action-oriented goals. (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)

There are several interpretations of the argument structure of Darwin's Origin of Species, representing Covering-Law, Inference-to-the-Best-Explanation, and (more recently) Semantic models. I argue that while all three types of interpretation enjoy some textual support, none succeeds in capturing the overall strategy of the Origin, consistent with Darwin's claim that it is 'one long argument'. I provide detailed criticisms of all three current models, and then offer an alternative interpretation based on the view that there are three main argument strategies in (...) the Origin, all supporting the 'causal efficacy' of Darwin's theory. This interpretation provides both a more unified treatment of the text, and some important implications concerning the relation between general philosophical models of scientific theory support and specific historical cases. (shrink)

In this review essay, I examine the central tenets of sociologist Dave Elder-Vass’s recent contribution to social ontology, as put forth in his book The Causal Power of Social Structures: Emergence, Structure and Agency. Elder-Vass takes issue with ontological individualists and maintains that social structures exist and have causal powers in their own right. I argue that he fails to establish his main theses: he shows neither that social structures have causal powers “in their own right” (in any sense of (...) this expression) nor that they exist. (shrink)

By force of _causes precede effects_, causality contributes to the temporal meaning of discourse. In case of semantic causal relations, this contribution is straightforward, but in case of epistemic causal relations, it is not. In order to gain insight into the semantics of epistemic causal relations, paradoxical cases are analyzed of text fragments in which temporal and causal meaning seem to be irreconcilable. A solution is proposed consisting of several parts: First, an analysis of epistemic causal coherence relations, in (...) which the effect sentence is argued to be a _belief_ sentence. Secondly, a method for deriving causal coherence relations. And thirdly, a format for representing temporal meaning in which contributions of tense and causality can be reconciled. Finally, it is argued that _understanding_ epistemically causally connected sequences is not equal to being persuaded to _believing_ them. The difference is shown to be expressible in the representation format proposed. (shrink)

One primary goal for metaphysical theories of natural kinds is to account for their epistemic fruitfulness. According to cluster theories of natural kinds, this epistemic fruitfulness is grounded in the regular and stable co- occurrence of a broad set of properties. In this paper, I defend the view that such a cluster theory is insufficient to adequately account for the epistemic fruitfulness of kinds. I argue that cluster theories can indeed account for the projectibility of natural kinds, but not for (...) several other epistemic operations that natural kinds support. Natural kinds also play a role in scientific explanations and categorizations. A theory of natural kinds can only account for these additional kind-based epistemic practices if it also analyzes their causal structure. (shrink)

Cet article consiste en un commentaire suivi de la section IV de l’ Enquête sur l’entendement humain. Il poursuit trois finalités : expliciter la structure de l’argumentation sceptique par laquelle Hume déconstruit la rationalité de l’inférence causale, éclairer le texte par certaines mises en perspectives historiques (en confrontant notamment Hume à Locke) et mettre en évidence le geste d’approfondissement de l’empirisme qui s’y joue : la théorie sceptique du raisonnement causal ne peut être atteinte qu’en dépassant l’expérience courante et immédiate (...) que chacun fait de son entendement. (shrink)

Using as a framework the logical treatment of causality in the Buddhist Madhyamika, a theory of the psychology of event coherence and causal connectedness is developed, and suggestive experimental evidence is offered. The basic claim is that events are perceived as coherent and causally bound to the extent that the outcome is seen to be already contained in the ground of the event in some form and the connecting link between them is seen as the appropriate means for changing (...) the outcome-in-the-ground to the outcome-as-perceived. There are four types of such connections: the identity of the object in the ground and outcome is seen as the same ; a property is seen to be transferred from ground to outcome ; for animate beings, a cognitive representation and a state of the world are seen to match - either the representation-as-outcome coming to match the world-as-ground or the world-as-outcome coming to match the representation-as-ground ; the `essence' of a category is seen to manifest itself . The standard critique of such coherence explanations is that they are tautological. We demonstrate that `nontautological' scientific accounts become convincing coherent explanations only to he extent that the outcome is re-introduced into the ground. Explanations which are noncausal altogether, such as probability or chance, are shown to be psychologically unstable. This critique suggests some new perspectives on causal thinking both in the cognitive sciences and the folk theories of daily life. (shrink)

The psychology of reasoning is increasingly considering agents' values and preferences, achieving greater integration with judgment and decision making, social cognition, and moral reasoning. Some of this research investigates utility conditionals, ‘‘if p then q’’ statements where the realization of p or q or both is valued by some agents. Various approaches to utility conditionals share the assumption that reasoners make inferences from utility conditionals based on the comparison between the utility of p and the expected utility of q. This (...) article introduces a new parameter in this analysis, the underlying causal structure of the conditional. Four experiments showed that causal structure moderated utility-informed conditional reasoning. These inferences were strongly invited when the underlying structure of the conditional was causal, and significantly less so when the underlying structure of the conditional was diagnostic. This asymmetry was only observed for conditionals in which the utility of q was clear, and disappeared when the utility of q was unclear. Thus, an adequate account of utility-informed inferences conditional reasoning requires three components: utility, probability, and causal structure. (shrink)

Sociologists are increasingly attentive to the mechanisms responsible for cause-and-effect relationships in the social world. But an aspect of mechanistic causality has not been sufficiently considered. It is well recognized that most phenomena of interest to social science result from multiple mechanisms operating in sequence. However, causal chains—sequentially linked mechanisms and their enabling background conditions—vary not just substantively, by the kind of causal work they do, but also structurally, by their formal properties. In this article, the author examines the (...) nature of causal chains, identifies major structural dimensions along which they differ, and makes a case that a mechanism-based explanation would be enhanced if causal chains and their structures were brought to the analytical forefront. (shrink)

Recently, a number of philosophers of science have claimed that much explanation in the sciences, especially in the biomedical and social sciences, is mechanistic explanation. I argue the account of mechanistic explanation provided in this tradition has not been entirely satisfactory, as it has neglected to describe in complete detail the crucial causal structure of mechanistic explanation. I show how the interventionist approach to causation, especially within a structural equations framework, provides a simple and elegant account of the causal structure (...) of mechanisms. This account explains the many useful insights of traditional accounts of mechanism, such as Carl Craver's account in his book Explaining the Brain , but also helps to correct the omissions of such accounts. One of these omissions is the failure to provide an explicit formulation of a modularity constraint that plays a significant role in mechanistic explanation. One virtue of the interventionist/structural equations framework is that it allows for a simple formulation of a modularity constraint on mechanistic explanation. I illustrate the role of this constraint in the last section of the paper, which describes the form that mechanistic explanation takes in the computational, information-processing paradigm of cognitive psychology. (shrink)

We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observed cause, one unobserved common cause, and two unobserved independent causes, depending on the pattern of associations and interventions they saw. (...) We replicated these findings with less‐informative training (Experiments 2 and 3) and a new apparatus (Experiment 3) to show that the pattern of data leads to hidden causal inferences across a range of prior constraints on causal knowledge. (shrink)

Through the introduction of his ‘common cause principle’ [The Direction of Time, 1956], Hans Reichenbach was the first to formulate a precise link relating causal claims to statements of probability. Despite some criticism, the principle has been hugely influential and successful—a pillar of scientific practice, as well as guiding our reasoning in everyday life. However, Bell’s theorem, taken in conjunction with quantum theory, challenges this principle in a fundamental sense at the microscopic level. For the same reason, the celebrated causal (...) model framework pioneered by Pearl, as well as by Spirtes, Glymour and Scheines, defies satisfactory causal explanations of quantum correlations—the role of the ‘causal Markov condition’ in this framework amounts to a generalisation of Reichenbach’s principle. Much effort has been devoted to the development of quantum causal models to overcome this challenge and to study whether a principled way of causal reasoning, albeit adjusted in its terms, can be maintained when it comes to quantum physics. A clarification of what quantum causal relations are supposed to be is also a much needed step in light of the hotly debated topic of causality in foundations of quantum theory, e.g. in the study of ‘indefinite causal structures’. This paper reports and discusses a framework of quantum causal models and argues that it is promising and interesting for two main reasons. First, it can be seen as a prime example of a recent research programme that took direct and fruitful inspiration from ideas first formulated by Reichenbach—it began by asking, and giving an answer to, how the common cause principle could be generalised appropriately to quantum theoretical terms. Second, the framework did not only facilitate generalisations of the core theorems of the classical framework of causal models, but, above all, it has a theorem at its center that justifies the ‘quantum causal Markov condition’ on the basis of what arguably is a natural definition of causal relations in quantum theory. This provides a conceptually clear grounding of the framework, as well as a proposal for the needed clarification of quantum causal relations in the foundations of quantum theory more generally. (shrink)

ABSTRACT The debate between proponents and opponents of causal foundationalism has recently surfaced as a disparity between causal structuralism and causal anti-foundationalism in the structural realist camp. The paper outlines and dissolves the problem of disparity for structural realism. I follow John Collier to specify causation in terms of the transmission of information. Unlike them, I built upon the reverse quantum data-processing inequality to show how this approach models causation as a symmetric process at the level of fundamental physics. I (...) show how this suggestion reduces the disparity about causation to a problem of application to diverse contexts. (shrink)

It is argued that in deterministic contexts evidence for causal relations states whether a boundary condition makes a difference or not to a phenomenon. In order to substantiate the analysis, I show that this difference/indifference making is the basic type of evidence required for eliminative induction in the tradition of Francis Bacon and John Stuart Mill. To this purpose, an account of eliminative induction is proposed with two distinguishing features: it includes a method to establish the causal irrelevance of boundary (...) conditions by means of indifference making, which is called strict method of agreement, and it introduces the notion of a background against which causal statements are evaluated. Causal statements thus become three-place-relations postulating the relevance or irrelevance of a circumstance C to the examined phenomenon P with respect to a background B of further conditions. To underline the importance of evidence in terms of difference/indifference making, I sketch two areas, in which eliminative induction is extensively used in natural and engineering sciences. One concerns exploratory experiments, the other engineering design methods. Given that a method is discussed that has been used for centuries, I make no claims to novelty in this paper, but hope that the combined discussion of several topics that are still somewhat underrepresented in the philosophy of science literature is of some merit. (shrink)

If the decades of the forties through the sixties were dominated by discussion of Hempel's “covering law“ explication of explanation, that of the seventies was preoccupied with Salmon's “statistical relevance” conception, which emerged as the principal alternative to Hempel's enormously influential account. Readers of Wesley C. Salmon's Scientific Explanation and the Causal Structure of the World, therefore, ought to find it refreshing to discover that its author has not remained content with a facile defense of his previous investigations; on the (...) contrary, Salmon offers an original account of different kinds of explications, advances additional criticisms of various alternative theories, and elaborates a novel “two-tiered“ analysis of explanation that tacitly depends upon a “two-tiered” account of homogeneity. Indeed, if the considerations that follow are correct, Salmon has not merely refined his statistical relevance account but has actually abandoned it in favor of a “causal/mechanistic“ construction. This striking development suggests that the theory of explanation is likely to remain as lively an arena of debate in the eighties as it has been in the past. (shrink)

What are scientific theories and how should they be represented? In this article, I propose a causal–structural account, according to which scientific theories are to be represented as sets of interrelated causal and credal nets. In contrast with other accounts of scientific theories (such as Sneedian structuralism, Kitcher’s unificationist view, and Darden’s theory of theoretical components), this leaves room for causality to play a substantial role. As a result, an interesting account of explanation is provided, which sheds light on (...) explanatory unification within a causalist framework. The theory of classical genetics is used as a case study. 1 Introduction2 The Theory of Classical Genetics3 Three Philosophical Accounts of the Theory of Classical Genetics3.1 The structuralist account3.2 Kitcher’s unificationism3.3 Darden and theory change in science4 A Common Lacuna: Where is Causality?5 Woodward’s Interventionist Account of Causation6 Causal Bayes Nets and Their Interrelations6.1 Causal Bayes nets6.2 Relations among causal nets6.3 Credal nets and their interrelations7 The Theory of the Gene and its Causal Graph8 A First Exemplar: Stem Length in Pea Plants8.1 Three crosses on stem length in pea plants8.2 The causal graph for stem length in pea plants8.3 Morgan’s explanatory principles and the credal net for stem length in pea plants9 Explaining Mendel’s Crosses: A Causal–Structural Account10 Monohybrid Crosses with Complete Dominance11 Exemplars,Explanatory Patterns, Generic Credal Nets, and Mechanism Schemas12 Incomplete Dominance13 Anomalies14 Multi-hybrid Crosses with Independent Assortment15 Multi-hybrid Crosses with Linkage and Crossing-Over16 Double Crossing-Over and the Linear Order of the Gene17 Causal–Structural Explanation18 Explanatory Unification19 Concluding Remarks. (shrink)

The aim of this talk unpublished until now, and that constitutes the last contribution of the author to this topic, is to show how Hans Reichenbach’s work “Die Kausalstruktur der Welt und der Unterschied zwischen Vergangenheit und Zukunft” , published in 1925, and significantly expanded and revised in the posthumously published book in 1956, The Direction of Time, inspires substantially part of the work that is carried out in the domain of causality at the beginning of the twenty first (...) century. (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)

The scope of this paper can be clarified by means of a well-known phenomenon that is usually called ‘industrial melanism’: the fact that the melanic form of the peppered moth became dominant in industrial areas in England in the second half of the nineteenth century. Such changes in relative phenotype frequencies are important explananda for population biologists. Apart from trying to explain such changes over time, population biologists also often try to explain differences between populations, e.g. why yellow shell colour (...) is dominant in certain colonies of land snails and almost absent in other colonies. The causal explanations that are given to address such explananda are the objects of analysis in this paper. Our primary aim is to explicate their structure: we want to capture the typical ingredients of causal explanations in population biology, and their organisation. Based on this explication, we discuss how natural selection fits into recent mechanical philosophy of science, and engage in the debate on the nature of evolutionary theory. (shrink)