Former discussions of biological generalizations have focused on the question of whether there are universal laws of biology. These discussions typically analyzed generalizations out of their investigative and explanatory contexts and concluded that whatever biological generalizations are, they are not universal laws. The aim of this paper is to explain what biological generalizations are by shifting attention towards the contexts in which they are drawn. I argue that within the context of any particular biological explanation or investigation, biologists employ two (...) types of generations. One type identifies causal regularities exhibited by particular kinds of biological entities. The other type identifies how these entities are distributed in the biological world. (shrink)

Is it in any way remarkable, and requiring explanation, that events fall into those orderly sequences which we call causal? The alleged problem of causal ordering often presents itself as follows. Of all conceivable universes, the vast majority would be scenes of chaos; why, then, is ours so well-regulated? Why does any situation change predictably, rather than in any one of the alternative logically possible ways? Scientific orderliness must, it is said, reflect metaphysical factors acting to filter out (...) disorder, for otherwise the systematic connection between events would be a matter of extraordinary coincidences. (shrink)

We present a logic, $$\mathbf {ELI^r}$$ ELI r, for the discovery of deterministic causal regularities starting from empirical data. Our approach is inspired by Mackie’s theory of causes as INUS-conditions, and implements a more recent adjustment to Mackie’s theory according to which the left-hand side of causal regularities is required to be a minimal disjunction of minimal conjunctions. To derive such regularities from a given set of data, we make use of the adaptive logics framework. (...) Our knowledge of deterministic causal regularities is, as Mackie noted, most often gappy or elliptical. The adaptive logics framework is well-suited to explicate both the internal and the external dynamics of the discovery of such gappy regularities. After presenting $$\mathbf {ELI^r}$$ ELI r, we first discuss these forms of dynamics in more detail. Next, we consider some criticisms of the INUS-account and show how our approach avoids them, and we compare $$\mathbf {ELI^r}$$ ELI r with the CNA algorithm that was recently proposed by Michael Baumgartner. (shrink)

The account of causal regularities in the influential INUS theory of causation has been refined in the recent developments of the regularity approach to causation and of the Boolean methods for inference of deterministic causal structures. A key element in the refinement is to strengthen the minimality or non-redundancy condition in the original INUS account. In this paper, we argue that the Boolean framework warrants a further strengthening of the minimality condition. We motivate our stronger condition by (...) showing, first, that a rationale for strengthening the original minimality condition in the INUS theory is also applicable to our proposal to go further, and second, that the new element of the stronger condition is a counterpart to a well-established minimality condition for probabilistic causal models. We also compare the various minimality conditions in terms of the difference-making criteria they imply and argue for the criterion implied by our condition. Finally, we show that putative counterexamples to our proposal can be addressed in the same way that the Boolean theorists defend the current minimality conditions in their framework. (shrink)

We present a logic, \, for the discovery of deterministic causal regularities starting from empirical data. Our approach is inspired by Mackie’s theory of causes as INUS-conditions, and implements a more recent adjustment to Mackie’s theory according to which the left-hand side of causal regularities is required to be a minimal disjunction of minimal conjunctions. To derive such regularities from a given set of data, we make use of the adaptive logics framework. Our knowledge of (...) deterministic causal regularities is, as Mackie noted, most often gappy or elliptical. The adaptive logics framework is well-suited to explicate both the internal and the external dynamics of the discovery of such gappy regularities. After presenting \, we first discuss these forms of dynamics in more detail. Next, we consider some criticisms of the INUS-account and show how our approach avoids them, and we compare \ with the CNA algorithm that was recently proposed by Michael Baumgartner. (shrink)

Two key ideas of scientific explanation - explanations as causal information and explanation as unification - have frequently been set into mutual opposition. This paper proposes a "dialectical solution" to this conflict, by arguing that causal explanations are preferable to non-causal explanations because they lead to a higher degree of unification at the level of the explanation of statistical regularities. The core axioms of the theory of causal nets are justified because they give the best (...) if not the only unifying explanation of two statistical phenomena: screening off and linking up. Alternative explanation attempts are discussed and it is shown why they don't work. It is demonstrated that not the core of TC but extended versions of TC have empirical content, by means of which they can generate independently testable predictions. (shrink)

How regular do mechanisms need to be, in order to count as mechanisms? This paper addresses two arguments for dropping the requirement of regularity from the definition of a mechanism, one motivated by examples from the sciences and the other motivated by metaphysical considerations regarding causation. I defend a broadened regularity requirement on mechanisms that takes the form of a taxonomy of kinds of regularity that mechanisms may exhibit. This taxonomy allows precise explication of the degree and location of regular (...) operation within a mechanism, and highlights the role that various kinds of regularity play in scientific explanation. I defend this regularity requirement in terms of regularity’s role in individuating mechanisms against a background of other causal processes, and by prioritizing mechanisms’ ability to serve as a model of scientific explanation, rather than as a metaphysical account of causation. It is because mechanisms are regular, in the expanded sense described here, that they are capable of supporting the kinds of generalizations that figure prominently in scientific explanations. (shrink)

Machamer, Darden, and Craver argue that causal explanations explain effects by describing the operations of the mechanisms which produce them. One of this paper’s aims is to take advantage of neglected resources of Mechanism to rethink the traditional idea that actual or counterfactual natural regularities are essential to the distinction between causal and non-causal co-occurrences, and that generalizations describing natural regularities are essential components of causal explanations. I think that causal productivity and regularity (...) are by no means the same thing, and that the Regularists are mistaken about the roles generalizations play in causal explanation. Humean, logical empiricist, and other Regularist accounts of causal explanation have had the unfortunate effect of distracting philosophers’ from important non-explanatory scientific uses of laws and lesser generalizations which purport to describe natural regularities. My second aim is to characterize some of these uses, illustrating them with examples from neuroscientific research. (shrink)

For a long time, regularity accounts of causation have virtually vanished from the scene. Problems encountered within other theoretical frameworks have recently induced authors working on causation, laws of nature, or methodologies of causal reasoning – as e.g. May (Kausales Schliessen. Eine Untersuchung über kausale Erklärungen und Theorienbildung. Ph.D. thesis, Universität Hamburg, Hamburg, 1999), Ragin (Fuzzy-set social science. Chicago: University of Chicago Press, 2000), Graßhoff and May (Causal regularities. In W. Spohn, M. Ledwig, & M. Esfeld (Eds.), (...) Current issues in causation (pp. 85–114). Paderborn: Mentis, 2001), Swartz (The concept of physical law (2nd ed.). http://www.sfu.ca/philosophy/physical-law/, 2003), Halpin (Erkenntnis, 58, 137–168, 2003) – to direct their attention back to regularity theoretic analyses. In light of the latest proposals of regularity theories, the paper at hand therefore reassesses the criticism raised against regularity accounts since the INUS theory of causation of Mackie (The cement of the universe. A study of causation. Oxford: Clarendon Press, 1974). It is shown that most of these objections target strikingly over-simplified regularity theoretic sketches. By outlining ways to refute these objections it is argued that the prevalent conviction as to the overall failure of regularity theories has been hasty. (shrink)

It is a crucial issue for history to determine a cause or a causal relation between two events. The regularity theory of causation is one of the most popular approach to this problem. The paper analyzes mainly how this approach deals with the determination of a causal condition and with the choice of the main cause from the remaining causal conditions. It examines also Mackie’s conception of cause as an INUS condition, which is in fact only one (...) version of the regularity theory. The paper not only outlines and illustrates these approaches, using a concrete causal statments, but also presents the criticism of their shortcomings. It points to their difficulties springing from their inability to distinguish between the laws and accidental generalizations. This is important, because a genuine causal relation cannot be based on the second type of statments. (shrink)

Though causality seems to have a natural place in chemical thought, the analysis of the underlying causal concepts requires attention to two different research styles. In Part One I attempt a classification and critical analysis of several philosophical accounts of causal concepts which appear to be very diverse. I summarize this diversity which ranges from causality as displayed in regular concomitances of types of events to causality as the activity of agents. Part Two is concerned with the analysis (...) of contrasting chemical discourses, comparing the classical atomist style of Boyle, and Lavoisier and von Liebig with the later energeticist style of van’t Hoff and Hinshelwood. In detail different clusters of causal concepts can be abstracted from these discursive styles, yet they all approximate the Realist format for causal discourse. By way of summary I make an attempt to map the vernacular distinctions of Part Two onto the philosophical territory of Part One. The argument is rounded off with a brief analysis of a chemical publication of 2008. (shrink)

Causal accounts of scientific explanation are currently broadly accepted (though not universally so). My first task in this paper is to show that, even for a causal approach to explanation, significant features of explanatory practice are not determined by settling how causal facts bear on the phenomenon to be explained. I then develop a broadly causal approach to explanation that accounts for the additional features that I argue an explanation should have. This approach to explanation makes (...) sense of several aspects of actual explanatory practice, including the widespread use of equilibrium explanations, the formulation of distinct explanations for a single event, and the tight relationship between explanations of events and explanations of causal regularities. (shrink)

The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a bygone’ nor ‘another fetish of modern science’; it still occupies a large part of the current debate in philosophy and the sciences. This investigation into causal modelling presents the rationale of causality, i.e. the notion that guides causal reasoning in causal modelling. It is argued that causal models are regimented by a rationale of variation, nor of regularity neither invariance, (...) thus breaking down the dominant Human paradigm. The notion of variation is shown to be embedded in the scheme of reasoning behind various causal models: e.g. Rubin’s model, contingency tables, and multilevel analysis. It is also shown to be latent – yet fundamental – in many philosophical accounts. Moreover, it has significant consequences for methodological issues: the warranty of the causal interpretation of causal models, the levels of causation, the characterisation of mechanisms, and the interpretation of probability. This book offers a novel philosophical and methodological approach to causal reasoning in causal modelling and provides the reader with the tools to be up to date about various issues causality rises in social science. (shrink)

We investigate the causal effects of religious service attendance on prosocial behaviours using longitudinal data from a nationally representative sample of 33,198 New Zealanders collected between 2018 and 2021. Our study innovates in three ways: (1) we use longitudinal rather than cross-sectional data; (2) we incorporate measures of help received alongside self-reported giving; and (3) our statistical models are designed to address causal questions, rather than simply to describe change over time. We model causal contrasts for three (...) hypothetical interventions – increasing, decreasing, or maintaining religious service attendance – and assess their effects on eight distinct prosocial domains. Study 1 focuses on self-reported charitable donations and volunteering. Studies 2 and 3 examine receiving help – both personal and financial – from family, friends, and the wider community. Across all analyses, we find that the causal effects of religious service attendance are notably smaller than cross-sectional correlations suggest. However, even modest increases in regular attendance would result in charitable donations equivalent to approximately 4% of the New Zealand Government’s annual spending – a considerable public benefit. By applying robust methods for causal inference to national-scale panel data, our study provides insights that can inform public policy about the social functions of religious participation and advances a methodological framework for investigating the social consequences of cultural practices. (shrink)

The Newtonian universe is usually understood to contain two classes of causal factors: universal regularitiesand initial conditions. I demonstrate that,in fact, the Newtonian universe contains no causal factors other thanuniversal regularities: the initial conditions ofany physical system are merely theconsequence of universal regularities acting on previoussystems. It follows that aNewtonian universe lacks the degree of contingency that is usually attributed to it. This is a necessary precondition for maintaining that the Newtonian universe is a block universe (...) that exhibits no temporal development. It follows also that Newtonian physics is inconsistent, since a Newtonian universe as a whole exhibits some properties – such as the total mass of the universe – that are not determined by the laws of Newtonian physics, and that must therefore be considered contingent. (shrink)

The majority of the currently flourishing theories of actual causation are located in a broadly counterfactual framework that draws on structural equations. In order to account for cases of symmetric overdeterminiation and preemption, these theories resort to rather intricate analytical tools, most of all, to what Hitchcock has labeled explicitly nonforetracking counterfactuals. This paper introduces a regularity theoretic approach to actual causation that only employs material conditionals, standard Boolean minimization procedures, and a stability condition that regulates the behavior of (...) class='Hi'>causal models under model expansions. Notwithstanding its lightweight analytical toolbox, this regularity theory performs at least as well as the structural equations accounts with their heavy appliances. (shrink)

This paper suggests and discusses an answer to the following question: What distinguishes causal from non-causal or coincidental co-occurrences? The answer derives from Elizabeth Anscombe’s idea that causality is a highly abstract concept whose meaning derives from our understanding of specific causally productive activities, and from her rejection of the assumption that causality can be informatively understood in terms of actual or counterfactual regularities.Keywords: Elizabeth Anscombe; Causality; Explanation; Inhibition.

The paper discusses the regularity account of causation but finds it insufficient as a complete account of our notion of causality. The attractiveness of the regularity account is its attempt to understand causation in terms of empirically accessible features of the world. However, this account does not match our intuition that singular causality is prior in normal epistemic situations and that there is more to causation than mere succession. Apart from succession and regularity, the concept of causality also contains a (...) modal feature which allows us to engage in counterfactual discourses about singular causal events and to claim that a particular cause is both sufficient and necessary for its effect in the circumstances. However, we may directly observe singular causes, but the modal element is not something we can possibly observe. Rather, this element is something we add to our perception of succession. Thus, the paper suggests that the modal feature of causality is a mental construction which was originally formed by our knowledge of certain structural features of similar events in other situations. It stems not from what we actually observe but from what we have observed or may observe under different but relevant circumstances. So the concept of causation has partly an empirical content and partly a constructed one. (shrink)

Systems involving many interacting variables are at the heart of the natural and social sciences. Causal language is pervasive in the analysis of such systems, especially when insight into their behavior is translated into policy decisions. This is exemplified by economics, but to an increasing extent also by biology, due to the advent of sophisticated tools to identify the genetic basis of many diseases. It is argued here that a regularity notion of causality can only be meaningfully defined for (...) systems with linear interactions among their variables. For the vastly more important class of nonlinear systems, no such notion is likely to exist. This thesis is developed with examples of dynamical systems taken mostly from mathematical biology. It is discussed with particular reference to the problem of causal inference in complex genetic systems, systems for which often only statistical characterizations exist. (shrink)

This thesis addresses the problem of causal asymmetry. This problem may be characterized as follows: what is the relation R such that if an event c causes an event e c bears relation R to e but e does not bear relation R to e. The traditional Humean account of causal asymmetry is that "R" may be replaced by "temporally prior." Difficulties with this account based on consideration of cases of simultaneous causation and backward causation have given rise (...) to non-Humean accounts of causal asymmetry. This dissertation analyzes some of these difficulties along with various non-Humean approaches. I offer a new theory of causal asymmetry which does not depend upon the temporal priority of causes to their effects and which differs from any of the other theories available. ;In chapter one the problem of causal asymmetry is clarified. This problem is distinguished from related issues. At the same time, I articulate the various philosophical considerations which bear on the scope and importance of this problem. I also state what I take to be the conditions an adequate account of causal asymmetry must satisfy. In the second chapter I discuss the classical Humean analysis of causal priority in terms of temporal priority. It is argued that arguments which have been put forward to show that backwards causation is logically impossible are unconvincing and that there are cases of simultaneous causation. In the third chapter the manipulability theory of causation is treated. Various formulations of this theory, from the most anthropomorphic to the least anthropomorphic are treated as they bear on the problem of causal asymmetry. Manipulability theory is found to be inadequate. ;In chapter four counterfactual theories of causation are considered. A number of counterexamples to these theories are presented and developed. In chapter five, two views which are grouped under "regularity theory" are analyzed and criticized: those of Bromberger and Berofsky. Mackie's earlier and later accounts of causal asymmetry along with Sanford's view are discussed in chapter six. In chapter seven, Erik Brown's recent view is considered alongside "transference theory." In the final chapter I develop a new theory of causal priority which utilizes the "circumstantial" character of the causal relation in accounting for causal asymmetry. (shrink)

In reply to certain cosmological arguments for theism, critics regularly argue that the causal principle ex nihilo nihil fit may be false. Various theistic counter-replies to this challenge have emerged. One type of strategy is to double down on ex nihilo nihil fit. Another, very different strategy of counter-reply is to grant for the sake of argument that the principle is false, while maintaining that sound cosmological arguments can be formulated even with this concession in place. Notably, one can (...) employ a weaker opening premise formulated in modal terms, proceeding for instance from the proposition that for any contingent object coming into existence it is at least possible that it have a cause. My aim here is to try out a related strategy for weakening the relevant opening premise. Granting that it is possible for a contingent object to come into existence out of nothing without a cause, I proceed from the extremely modest claim that the obtaining of exceptionless longstanding contingent regularities demands an explanation. As such, the contingent regularity that empirically accessible macro-level contingent objects do not pop into existence causelessly demands explanation. And as it turns out, that explanation will have to be in terms of an object or objects possessed of at least some of the traditional divine attributes. (shrink)

In this paper, we propose a regularity theory of causation. The theory aims to be reductive and to align with our pre‐theoretic understanding of the causal relation. We show that our theory can account for a wide range of causal scenarios, including isomorphic scenarios, omissions, and scenarios which suggest that causation is not transitive.

The aim of the causal dynamical triangulations approach is to define nonperturbatively a quantum theory of gravity as the continuum limit of a lattice-regularized model of dynamical geometry. My aim in this paper is to give a concise yet comprehensive, impartial yet personal presentation of the causal dynamical triangulations approach.

Traditionally, philosophers have cashed out the distinction between law-like and accidental regularities sharply: a regularity is either law-like, and thereby necessary, or accidental. However, Mitchell and Lange have drawn attention to the fact that some law-like regularities come in different degrees of necessity. For instance, the regularity expressed by “all electrons are negatively charged” has a greater degree of necessity than the one expressed by “all mammals are warm-blooded”, even if both of them are true. Moreover, Mitchell argues (...) that the dichotomy between accidental and necessary regularities is unable to capture the complexity of the causal structure of the world. Building on this, I argue that regularities do not only come in different degrees of necessity, but also have different formal features and ontological features. All these features matter in order to make sense of the causal complexity of the world. Accordingly, I propose a new conceptual framework to analyze regularities according to three mutually independent levels of analysis: formal features, degree of necessity, and ontological grounds. This new framework can make sense of different degrees of necessity, and it naturally accommodates a wide variety of scientifically-relevant regularities including those typically associated with laws of nature, biological mechanisms, and dispositional properties. (shrink)

In this paper I explore the relationship between the “Humean” regularity view of causation, the view that a cause is a necessary condition of its effect, and the asymmetry of causation—the principle that if an event e1 causes e2, then it is false that e2 causes e1. I argue that the regularity view, in combination with the view that a cause is a necessary condition of its effect, is inconsistent with the asymmetry of causation, and that the inconsistency can be (...) removed by a modification of the view that a cause is a necessary condition of its effect that captures what is plausible in that view. I defend Hume, then, against the objection that he cannot accommodate a cause as being a necessary condition of its effect without absurdly denying the asymmetry of causation. This is only a limited defense, though, for I do not address the issue on which the tenability of the regularity view ultimately depends: viz., whether it can distinguish between causal laws and accidental generalizations without appealing to natural necessity. (shrink)

Regularity theories of causation assert that causal or nomic notions are to be reduced into “mere” frequencies of particular, non-nomic, co-located qualities and matters of fact. In this essay, I present a critical exploration of Armstrong and Strawson’s explanatory arguments against regularity theories. The shortcomings of these older arguments for nomic realism are identified and a revamped version which is immune to such problems is outlined and defended. I argue that anti-realism suffers substantial disconfirmation due to its comparative inability (...) to unify empirical regularities in the absence of any probabilistic counterweights. I also show that realist theories are much more probable than their anti-realist competitors both individually and in aggregate. This is shown to be the case with even the most humble of observational data. This revamped argument is Bayesian in character; it is immune to the criticisms of Beebee, Everitt, Loewer, and van Fraassen ; and it is empiricist friendly to boot. (shrink)

This paper replies to Peter Millican (Mind, 2009), who argues that Hume denies the possible existence of causal powers which underlie the regularities that we observe in nature. I argue that Hume's own philosophical views on causal power cannot be considered apart from his mitigated skepticism. His account of the origin of the idea of causal power, which traces it to a subjective impression, only leads to what he calls ‘Pyrrhonian scepticism’. He holds that we can (...) only escape such excessive skepticism by way of a natural judgment based on the association of ideas, which forms the basis of what he calls ‘a legitimate ground of Assent’. (shrink)

Minimally, causal realism (as understood here) is the view that accounts of causation in terms of mere, regular or probabilistic conjunction are unsatisfactory, and that causal phenomena are correctly associated with some form of de re necessity. Classic arguments, however, some of which date back to Sextus Empiricus and have appeared many times since, including famously in Russell, suggest that the very notion of causal realism is incoherent. In this paper I argue that if such objections seem (...) compelling, it is only because everyday expressions concerning causal phenomena are misleading with respect to certain metaphysical details. These expressions generally make reference to the relations of events or states of affairs, but ignore or obscure the role played by causal properties. I argue that on a proposed alternative, an analysis in terms of causal processes, more refined descriptions of causal phenomena escape the charge of incoherence. Causal necessity is here located in the relations of causal properties. I distinguish this view from the recent process theories of Salmon and Dowe, which are disinterested in causal realism. (shrink)

It has been nearly a decade and a half since Fred Dretske, David Armstrong and Michael Tooley, having each rejected the Regularity theory, independently proposed that natural laws are grounded in a second-order relation that somehow binds together universals.' (l shall call this the ‘DTA theory’). In this way they sought to overcome the major - and notorious — shortcomings of every version of the Regularity theory: how to provide truth conditions for laws that lack instances; how to distinguish laws (...) from accidental generalizations; how to provide truth conditions for the counterfactuals and disposition statements that laws apparently ‘support’; how to justify inductive inferences from past events to laws and future events. For each of these puzzles, an apparently key element in the solution seems to be missing from Regularity theories. That missing element is a genuine connection, a relation with more than merely spatial and/or temporal content, linking the antecedent of a law to its consequent. Once such an additional objective element - however understood — is admitted to be essential to the analysis of laws, one is forced to give up the idea that the logical form of laws can be given in terms of quantifiers ranging over events or states of alfairs, and truth-functions. (shrink)

The paper examines definitions of ‘cause’ in the epidemiological literature. Those definitions all describe causes as factors that make a difference to the distribution of disease or to individual health status. In the philosophical jargon, causes in epidemiology are difference-makers. Two claims are defended. First, it is argued that those definitions underpin an epistemology and a methodology that hinge upon the notion of variation, contra the dominant Humean paradigm according to which we infer causality from regularity. Second, despite the fact (...) that causes be defined in terms of ‘difference-making’, this cannot fixes the causal metaphysics. Causality in epidemiology ought to be interpreted according to the epistemic theory. In this approach relations are deemed causal depending on the evidence and on the available methods. Indeed, evidence to establish causal claims requires difference-making considerations; furthermore, those definitions of cause reflect the ‘variational’ epistemology and methodology of epidemiology. (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)

The prediction of future events from our knowledge of past events is one of the main functions of Science. Such predictions are made possible by inferring causal relations between events from observed regularities. These relations are then codified into “laws of nature,” and it is through knowledge of these laws that prediction becomes possible. The concept of “causal relation” is thus a fundamental one in the structure of science. Now recent advances in physics have led scientists to (...) modify considerably their views of this concept. It has been found necessary to abandon the idea of rigidly deterministic causal relations, and substitute in its stead relations that are only determined with a certain degree of probability. The main purpose of this paper is to analyse the meaning of these changes in our concepts of causality. (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)

We reassess Woodward’s counterfactual account of explanation in relation to regularity explananda. Woodward presents an account of causal explanation. We argue, by using an explanation of Kleiber’s law to illustrate, that the account can also cover some noncausal explanations. This leads to a tension between the two key aspects of Woodward’s account: the counterfactual aspect and the causal aspect. We explore this tension and make a case for jettisoning the causal aspect as constitutive of explanatory power in (...) connection with regularity explananda. (shrink)

The last few decades have witnessed intense debates in Hume scholarship concerning Hume’s account of causation. At the core of the “old–new Hume” debate is the question of whether causation for Hume is more than mere regularity, in particular, whether Hume countenances necessary connections in mind-independent nature. This chapter assesses this debate against the background of Hume’s “foundational project” in the Treatise. The question of the role and import of Hume’s account of the idea of cause is examined and compared (...) with Hume’s treatment of other ideas. (shrink)

A regularity theory of causation analyses type-level causation in terms of Boolean difference-making. The essential ingredient that helps this theoretical framework overcome the problems of Hume’s and Mill’s classical accounts is a principle of non-redundancy: only Boolean dependency structures from which no elements can be eliminated track causation. The first part of this paper argues that the recent regularity theoretic literature has not consistently implemented this principle, for it disregarded an important type of redundancies: structural redundancies. Moreover, it is shown (...) that a regularity theory needs to be underwritten by a hitherto neglected metaphysical background assumption stipulating that the world's causal makeup is not ambiguous. Against that background, the second part then develops a new regularity theory that does justice to all types of redundancies and, thereby, provides the first all-inclusive notion of Boolean difference-making. (shrink)

In this thesis I investigate causal inquiry in the social sciences, drawing on examples from various disciplines and in particular from conflict studies. In a backlash against the pervasiveness of statistical methods, in the last decade certain social scientists have focused on finding the causal mechanisms behind observed correlations. To provide evidence for such mechanisms, researchers increasingly rely on ‘process tracing’, a method which attempts to give evidence for causal relations by specifying the chain of events connecting (...) a putative cause and effect of interest. I will ask whether the causal claims process tracers make are defensible, and where they are not defensible I will ask how we can improve the method. Throughout these investigations, I show that the conclusions of process tracing are constrained both by the causal structure ofthe social world and by social scientists’ aims and values. My central argument is this: all instances of social phenomena have causally relevant differences, which implies that any research design that requires some comparison between cases is limited by how we systematize these phenomena. Moreover, such research cannot rely on stable regularities. Nevertheless, to forego causal conclusions altogether is not the right response to these limitations; by carefully outlining our epistemic assumptions we can make progress in causal inquiry. While I use philosophical theories of causation to comment on the feasibility of a social scientific method, I also do the reverse: by investigating a popular contemporary method in the social sciences, I show to what extent our philosophical theories of causation are workable in practice. Thus, this thesis is both a methodological and a philosophical work. Every chapter discusses both a fundamental philosophical position on the social sciences and a relevant case study from the social sciences. (shrink)

It is widely thought that Bayesian confirmation theory has provided a solution to Hempel's Paradox (the Ravens Paradox). I discuss one well‐known example of this approach, by John Mackie, and argue that it is unconvincing. I then suggest an alternative solution, which shows that the Bayesian approach is altogether mistaken. Nicod's Condition should be rejected because a generalisation is not confirmed by any of its instances if it is not law‐like. And even law‐like non‐basic empirical generalisations, which are expressions of (...) assumed underlying causal regularities, are not so confirmed if they are absurd in the light of our causal background knowledge or if their instances are not also possible instances of the relevant causal claim. (shrink)

Traditionally, Hume has widely been viewed as the standard-bearer for regularity accounts of causation. But between 1983 and 1990, two rival interpretations appeared—namely the skeptical realism of Wright, Craig, and Strawson, and the quasi-realist projectivism of Blackburn—and since then the interpretative debate has been dominated by the contest between these three approaches, with projectivism recently appearing the likely winner. This paper argues that the controversy largely arose from a fundamental mistake, namely, the assumption that Hume is committed to the subjectivity (...) of our conception of causal necessity. That assumption generated tensions within the regularity account, which the skeptical realist and quasi-realist alternatives, in very different ways, purported to resolve. But a broader and more balanced view of the textual evidence, taking due account of the relatively neglected sections where Hume applies the results of his analysis, tells strongly in favour of an objectivist regularity view, both in respect of causation and causal necessity. Despite some complications, the upshot is a far more straightforward reading of Hume than those that have hitherto dominated this long-running debate. (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)

Central passages: Cicero On Fate 11–17; Alexander of Aphrodisias On Fate 208.15–21. Critics of the Stoics tried to show up an inconsistency between Chrysippus’ acceptance of divination, on the one hand, and his conception of contingency, on the other. The argument claims that, since theorems of divination connect, in a conditional, a proposition about the past with one about the future, and since the necessity of the former is transferred to the latter, future occurrences—as far as covered by divination—are all (...) necessary. In the first part of this chapter, the argument is analysed in detail, and several logical difficulties contained in it are unravelled. The second part is concerned with Chrysippus’ counter‐argument. By determining that divinatory theorems are correctly expressed as negated conjunctions rather than conditionals, Chrysippus successfully blocks his opponents’ argument: what is genuinely predicted by diviners is not necessitated through the prediction, because the relation between sign and predicted future event does not ‘transmit’ necessity. Universal regularity between sign and predicted event does not suffice to make a future event necessary. There is no ‘causal chain’ connecting them. Theorems of other sciences, for example, medical theorems that express an empirical connection between things/events, make true Chrysippean conditionals. However, human freedom as conceived by the Stoics is not thereby threatened. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:141 HUME AND CAUSAL INFERENCE Hume held that any time we reach by inference a belief about the existence or qualities of some object or event that we have not actually perceived, the inference is grounded in beliefs about causal relations holding between the object of belief and some other object or objects that have actually been experienced. I will examine here Hume's account of such (...) class='Hi'>causal inferences. There are many places in the Treatise of Human Nature at which Hume seems to claim that causal inferences take place when and only when the percipient has had an extensive previous experience of objects of the respective types as regularly conjoined in relations of contiguity and temporal priority. It seems to me that reliance on these passages alone gives a distorted view of Hume's theory of causal inference. Contrary to appearances, Hume did not hold that extensive previous experiences of objects of the same types as properly conjoined is either necessary or sufficient for causal inference. Hume's view is more subtle and complicated, and there are important considerations of coherence and holism present in it. Getting Hume's view on this matter clear is important because it shows how he can escape some of the objections that have been laid against his theory, and it makes possible a reading of Hume as a causal realist. Hume's theory of inference provides the resources for drawing a distinction between accidental and genuine causal regularities and it is compatible with the view that causal relations (and not mere de facto regularities) hold among events in the world independently of their being perceived. The examination begins by looking at a different, but related, issue. Why does Hume think 142 that of the three types of association, only cause and effect association is capable of producing belief? Hume puts the question in the form of a possible objection to his account. For it may be said, that if all the parts of that hypothesis be true, viz. that these three species of relation are deriv'd from the same principles; that their effects in inforcing and inlivening our ideas are the same; and that belief is nothing but a more forcible and vivid conception of an idea; it shou'd follow, that that action of the mind may not only be deriv'd from the relation of cause and effect, but also from those of contiguity and resemblance. But as we find by experience, that belief arises only from causation, and that we can draw no inference from one object to another, except they be connected by this relation, we may conclude, that there is some error in that reasoning, which leads us into such difficulties. (T 107) It is important to note that Hume is talking here only about beliefs about the existence or situations of objects that have not been intuited or experienced. Beliefs about relations of ideas (e.g., that 2 + 2 = 4) are intuitions (or perhaps memories 2 of intuitions). Beliefs in the existence of things as experienced or in the previous existence of things as previously experienced automatically have the 3 vivacity of impressions or memory ideas. What Hume takes himself to need to explain is why cause and effect association allows for a transfer of this vivacity to an idea of an object or situation that has not itself been intuited or experienced and why 4 resemblance and contiguity association do not. If I see a green ball next to a red sweater, then I will normally subsequently believe that the green ball existed next to the red sweater at that time (at 143 least until my memories begin to lose vivacity). This is purely a matter of sense experience and memory. Now if I subsequently see a different red sweater (or the same sweater in a different location), I may be led to think of a green ball existing next to it purely through contiguity and resemblance association. But presumably I would not believe that a green ball existed next to the currently perceived red sweater unless I currently perceived a green ball or my belief was based on some 5 form of causal inference... (shrink)

Approximately one in six top economic research papers draws an explicitly causal conclusion. But what do economists mean when they conclude that A 'causes' B? Does 'cause' say that we can influence B by intervening on A, or is it only a label for the correlation of variables? Do quantitative analyses of observational data followed by such causal inferences constitute sufficient grounds for guiding economic policymaking? The Philosophy of Causality in Economics addresses these questions by analyzing the meaning (...) of causal claims made by economists and the philosophical presuppositions underlying the research methods used. The book considers five key causal approaches: the regularity approach, probabilistic theories, counterfactual theories, mechanisms, and interventions and manipulability. Each chapter opens with a summary of literature on the relevant approach and discusses its reception amongst economists. The text details case studies, and goes on to examine papers which have adopted the approach in order to highlight the methods of causal inference used in contemporary economics. It analyses the meaning of the causal claim put forward, and finally reconstructs the philosophical presuppositions accepted implicitly by economists. The strengths and limitations of each method of causal inference are also considered in the context of using the results as evidence for policymaking. This book is essential reading to those interested in literature on the philosophy of economics, as well as the philosophy of causality and economic methodology in general. (shrink)

What types of entities qualify as causes and effects? What is the relationship between cause and effect? How are causal claims to be assessed? The first question deals with the structure of the world; the second is about theories that interpret the relationship of causes to effects; while the third has to do with proper procedure in science and everyday life. This volume is a wide-ranging history of answers that have been given to these three questions, and their relationship (...) to scientific understanding.Losee presents a number of theories of causality within a historical survey that emphasizes the interrelationship between these theories and developments in science. His analysis displays the strengths and weaknesses of these theories so as to contribute to our present understanding of causal relatedness.Among the positions discussed are those of Aristotle, Hume, Kant, Mill, Salmon, Lewis, and Woodward. Losee's analysis displays the strengths and weaknesses of theories that identify causal relatedness with regularity of sequence, probability increase, energy transfer, exchange of a conserved quantity, counterfactual dependence, and inferability.These theories are judged, in part,by their ability to resolvedifficulties posed by instances of overdetermination,causation by omission, preventive causation, and causation by disconnection. Since applications of the theories to these instances disagree, a strategy of employing multiple concepts of causation is examined.Theories of Causality also describes the particular difficulties for causal analysis posed by quantum mechanics. One such difficulty is the prohibition against combining a causal analysis of a quantum process with a spatio-temporal description of that process. (shrink)

In the philosophical tradition, the notions of determinism and causality are strongly linked: it is assumed that in a world of deterministic laws, causality may be said to reign supreme; and in any world where the causality is strong enough, determinism must hold. I will show that these alleged linkages are based on mistakes, and in fact get things almost completely wrong. In a deterministic world that is anything like ours, there is no room for genuine causation. Though there may (...) be stable enough macro-level regularities to serve the purposes of human agents, the sense of “causality” that can be maintained is one that will at best satisfy Humeans and pragmatists, not causal fundamentalists. (shrink)

This provocative but persuasive book is essentially a radical attack upon the Humean conception of causality and the presentation and defense of a counter-theory, closer to everyday experience and pre-Humean traditional views. As formulated by empiricist philosophers, the Humean approach depends on two basic postulates. The philosophical analysis of any non-empirical concept must be a formal explication; any residue elements have to be accounted for in terms of their psychological origins. The world as experienced can be conceived adequately as a (...) logically independent system of things or flux of events, without the unwarranted assumption that individuals persist diachronically. As the grounds for undermining these assumptions, the authors develop a conception of causes as "powerful particulars," i.e., things which have both a nature and powers. So long as the nature remains unchanged the agent in question will continue to behave in this fashion with a natural necessity, stemming from the individual’s nature and specific powers. The opening chapter discusses the problem of conceptual and natural necessity—as distinct from logical necessity which alone is allowed by the Humean empiricists. Natural necessity is the mark of the relationship between real causes and their respective effects, whereas conceptual necessity characterizes the way our statements about such are themselves related. Later the irreducibility of natural necessity is emphasized and its differences from logical entailment spelled out. Chapter two takes up the subject of the "regularity theory and its allies." Characteristic of such are two claims: the empirical content of a causal-relationship statement is exhausted by the actual or hypothetical regularity between independent entities, and the necessity ordinarily attributed to causal production is an illusion, to be accounted for in various ways. Subsequent chapters are devoted to assaulting the pillars of the Humean notion either directly or indirectly through an illuminating and attractive account of their own theory of nature, causal powers, and natural necessity. The final chapter, entitled "Fields of Potential," indulges in speculation about the nature of ultimate entities on the basis of an extended generalization of the notion of the powerful individual, and concludes with a brief account of the historical antecedents of Faraday’s modern field theory and the metaphysical implications of a generalized field theory.—A.B.W. (shrink)

The ArgumentEinstein's concept of causality as analyzed in this paper is a thick concept comprised of: (a) regularity; (b) locality; (c) symmetry considerations leading to conservation laws; (d) mutuality of causal interaction. The main theses are: 1. Since (b)–(d) are not elements of Hume's concept of causality, Einstein's concept, the concept embedded in the theory of relativity, is manifestly non–Humean. 2. On a Humean conception, Newtonian mechanics is a paradigmatically causal theory. Einstein, however, regarded this theory as causally (...) deficient, for it fails to comply with both (b) and (d). Special relativity was (partly) motivated by the wish to correct the first of these failures; general relativity the second. 3. Ironically, general relativity, based on the thick concept of causality, opens the way for a conventionalist understanding of that concept. 4. With regard to human freedom, Einstein professed to be a Spinozist. However, he suggested a version of soft determinism, not found in Spinoza. (shrink)

In The Secret Connexion1 Galen Strawson argues against the traditional interpretation of Hume, according to which Hume’s theory of meaning leads him to a regularity theory of causation. In actual fact, says Strawson, ‘Hume believes firmly in some sort of natural necessity’ (p. 277). What Hume denied was that we are aware of causal connections outrunning regular succession, and that we have a ‘positively or descriptively contentful conception’ of such powers (p. 283); he did not deny that there are (...) such powers, or that they are what we are talking about when we talk about causation. Strawson has four central lines of argument. His ‘most direct evidence’ (p. 2) against a regularity interpretation consists of (1) passages where Hume refers to hidden powers underlying the regularities of which we are aware. Strawson’s broader motivations for rejecting the traditional interpretation are (2) that the regularity theory is in itself quite absurd, and (3) that it is incompatible with Hume’s ‘non-committal scepticism’. And the method which he uses to defend his interpretation against pressure from the theory of ideas is (4) to develop some comments of Hume’s on ‘relative’ ideas into something like a further theory of content to supplement the theory of ideas. Strawson develops almost the strongest case I can imagine for his claims. I shall try to explain why he leaves me unconvinced. (shrink)