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Agency and interventionist theories of causation take as their point of departure a common-sense idea about the connection between causation and manipulation: causal relationships are relationships that are potentially exploitable for purposes of manipulation and control. Very roughly, if C causes E then if C were to be manipulated in the right way, there would be an associated change in E. Conversely, if there would be a change in E, were the right sort of manipulation of C to occur, then C causes E. Accounts of causation in this vein have been defended by Collingwood, Gasking, and others. Similar ideas are defended by many social scientists and by some statisticians and theorists of experimental design.

Aristotle's Physics is a study of nature (phusis) and of natural objects (ta phusei). According to him, these objects—either all of them or at least some of them—are in motion. That is, they are kinoumena, things that are subject to change. The first book of the Physics is largely devoted to this task. The account of substantial change in the Physics is devoid of any commitment to prime matter. Aristotle also takes up the topics of alteration and coming-to-be in De Generatione et Corruptione. He adopts a kind of conservation principle: “the corruption of one thing is the generation of another, and vice versa.” In addition, Aristotle points out that all changes involve both a subject (hupokeimenon) and an attribute (pathos) of a sort which can be predicated of the subject, and says that either one of these is capable of “change” (metabolê).

Anti-reductionism is the view that causation cannot be analysed non-nomically and, further, that causation still resists analysis even when the non-causal, nomic concepts are made available. In other words, the anti-reductionist maintains that there can be no non-causal analysis of causation. Indeed, some anti-reductionists hold that causation does not supervene on the non-causal facts. This article is an overview and defence of anti-reductionism. It locates anti-reductionism relative to some possible companion doctrines and recounts the development of anti-reductionism.

This chapter considers the nature of the causal asymmetry, or even more generally, the asymmetry of influence. Putting aside explanations which would appeal to an asymmetry in time as explaining this asymmetry, it aims to show, using current physical theory and no ad hoc time asymmetric assumptions, why it is that future-directed influence sometimes advances one's goals but backward-directed influence does not. The chapter claims that agency is crucial to the explanation of the influence asymmetry. It provides an exhaustive account of the advancement asymmetry that is connected with fundamental physics, influence, causation, counterfactual dependence, and related notions in palatable ways.

Presocratic atomism was one of the most influential of the early theories: both Plato and Aristotle thought of it as a major competing theory, and it was an important source for post-Aristotelian Hellenistic theories. It has been commonplace that the atomism developed first by Leucippus of Abdera and then by Democritus of Abdera was a reaction to the Eleatic arguments of Zeno and Melissus, but the details of that influence have sometimes seemed rather hazy. This article brings them into sharper focus. This article considers the Eleatic foundations of atomism, especially the question of the importance of Zeno and Melissus for Democritus. By concentrating on some of the less-studied aspects of atomism and especially of the development of the concept of the unlimited into the notion of the infinite, it furthers the understanding of not only the development of early atomism but also the Eleatics Zeno and Melissus.

Watson and Crick's discovery of the structure of DNA transformed biology by providing a basis for explaining a wide variety of phenomena. Philosophical discussion concerning this discovery can be organized around two opposing views: theoretical reductionism and layer-cake antireductionism. The view about genetics that emerges from “theoretical reductionism” is of a two-tiered science: an upper tier of theoretical principles associated with the classical theory of genetics and a lower tier of theoretical principles about molecular processes involving DNA. The view about genetics that emerges from antireductionist view is also of a two-tiered science: an upper tier of theoretical principles aimed at explaining transmission phenomena and a lower tier of theoretical principles aimed at explaining phenomena of replication and expression of the genetic material.

This article describes the methods for modeling causal effects in observational social science. It considers the capacity of new graphical methods to represent and then motivates models that can effectively deliver estimates of the underlying heterogeneity of causal effects. The major advancements that have allowed scholarship to move beyond simple regression models are also elaborated. There are two basic goals of writing down a causal graph: (1) to present the set of causal relationships implied by the available state of knowledge, (2) to evaluate the feasibility of alternative estimation strategies. Causal graphs can obscure important distinctions precisely due to their flexibility. Thus, their flexibility enables careful and precise consideration of the challenges of causal effect identification, separated in helpful ways from many specification issues that are less fundamental.

This article focuses on the thesis known as the causal closure (or causal completeness) of physics (CoP)—that all physical events can be fully explained by physical causes governed by the fundamental laws of physics. This thesis raises well-known questions central to free-will debates about the nature and possibility of the “mental causation” of physical events (e.g., beliefs, desires, intentions). If all causes are physical causes, as CoP implies, it would seem that psychological states or events must be fully reducible to physical events or they would be epiphenomenal. The discussion also introduces a notion of “contextual emergence” (according to which lower-level descriptions of events in physical terms contain necessary, but not sufficient, conditions for higher-level descriptions in mental terms) and argues that such a notion of contextual emergence allows one to answer objections to the possibility of mental causation.

‘Causal modelling’ is a general term that applies to a wide variety of formal methods for representing, and facilitating inferences about, causal relationships. The end of the twentieth century saw an explosion of work on causal modelling, with contributions from such fields as statistics, computer science, and philosophy; as well as from more subject-specific disciplines such as econometrics and epidemiology. This article focuses on two programmes that have attracted considerable philosophical attention, one due to the computer scientist Judea Pearl and his collaborators, and the other to the philosophers Peter Spirtes, Clark Glymour, and Richard Scheines. It offers a much simplified presentation of causal models that emphasizes various points of philosophical interest.

Causal pluralism is the view that causation is not a single kind of relation or connection between things in the world. Instead, the apparently simple and univocal term ‘cause’ is seen as masking an underlying diversity. Assessing such a claim requires making sense of a difficult counting operation. How do we tell whether a theory of causation is identifying causation with a ‘single’ kind of connection? In practice, there tends not to be much disagreement about how to do the counting, because most philosophical work on causation has sought a view with an obvious kind of unity. The literature often works with a standard range of candidate connections that seem to have an important link to the idea of causation.

A dispositional ontology, admitting a category of power or capacity, is thought by some to offer a vital insight into the nature of causation. Proponents believe that other ontologies lack the metaphysical resources to capture this insight. At its most ambitious, a causal powers ontology purports to offer a solution to, or dissolution of, the problem of causation. The argument is that the traditional problem of causation is generated by a faulty Humean ontology in which the world is described as a sum of ‘loose and separate’ distinct existences. Once the main Humean premise is accepted, of there being no necessary connections between distinct existences, then the notion of causation becomes immediately problematic.

If the core idea of process theories of causation is that causation can be understood in terms of causal processes and interactions, then the approach should be attributed primarily to Wesley Salmon (1925–2001). Salmon takes causal processes and interactions as more fundamental than causal relations between events. To express this Salmon liked to quote John Venn: ‘Substitute for the time honoured “chain of causation”, so often introduced into discussions upon this subject, the phrase a “rope of causation”, and see what a very different aspect the question will wear’. According to the process theory, any facts about causation as a relation between events obtain only on account of more basic facts about causal processes and interactions. Causal processes are the world-lines of objects, exhibiting some characteristic essential for causation.

This article argues that the intrinsicness of the causal relation undermines the main case for facts as the causal relata, which is based on causation by and of absences. Furthermore, it argues that since causes and effects are generally temporally and spatially related to each other, facts could not be causes and effects. It also argues that the transitivity of causation rules out at least one major candidate for causal relata, coarse-grained events. And, finally, it argues that since the best theory of causation employs the notion of qualitative or property persistence, the best candidate for causal relata must be based around tropes or particularized properties.

This article explains the puzzling methodology of an important econometric study of health and status. It notes the widespread use of invariance in both economic and philosophical studies of causality to guarantee that causal knowledge can be used to predict the effects of manipulations. It argues that the kind of invariance seen widely in economic methodology succeeds at this job whereas a standard kind of invariance now popular in philosophy cannot. It questions the special role of causal knowledge with respect to predictions about the effects of manipulations once the importance of adding on invariance is recognized. It also draws the despairing conclusion that both causation and invariance are poor tools for predicting the outcomes of policy and technology and to pose the challenge.

There has been in the last decade or so an upsurge of interest in causation outside of philosophy. One important strand of research focuses on how statistical data can be used to draw inferences about causal structures. Central to this approach are ‘causal models’, intended to represent systems of ‘variables’ connected by ‘mechanisms’. By careful appeal to and analysis of such causal models, it is possible to develop subtle ways of empirically testing causal hypotheses in light of statistical data. But two serious problems as yet prevent this approach from attaining the kind of scientific rigour it ought to have. Both are foundational. First, crucial notions — most notably, the notion of a ‘mechanism’ — are left almost wholly obscure, in a way which makes it impossible to say anything general or informative about what makes any given situation apt for description by one causal model rather than another. Secondly, the way causal models are typically used draws no distinction whatsoever between ordinary causal processes and causal connections involving omissions.

Aquinas used the term ‘agent’ referring to a created substance or to God. Aquinas's concept of substance explains that substances are set apart from accidents by the fact that substances are subsistent things. Aquinas believed that each substance belong to a particular species and has a complete nature common to any other members of that species that there may be. He also mentioned that there is a distinctive set of causal powers corresponding to each specific nature. Aquinas called the change (or motion) produced by the agent the ‘passion’. Aquinas considered active powers as real (though not necessarily physical) components of a thing that enable it to act in certain ways. A passive power is something posited to account for the fact that a thing is capable of being acted upon in a certain way, that is, to account for the fact that a thing is capable of undergoing a certain sort of passion. Aquinas claimed that every agent (living and nonliving) acts by intending some end. Aquinas did not think that inanimate objects do things out of an awareness of some goal. Aquinas distinguished two types of inclinations that include natural and voluntary. The types of natural inclinations are fire's inclination to heat and a stone's inclination to fall. A voluntary inclination is just any act of the will.

In its simplest form, a causal model of explanation maintains that to explain some phenomenon is to give some information about its causes. This prompts four questions that will structure the discussion to follow. The first is whether all explanations are causal. The second is whether all causes are explanatory. The answer to both of these questions turns out to be negative, and seeing why this is so helps to clarify the relationship between causation and explanation. The third question is itself a request for an explanation: Why do causes explain, when they do? Why, for example, do causes explain their effects but effects not explain their causes? Finally, the article considers how explanation can illuminate the process of causal inference.

This article briefly discusses Hume's original argument concerning the absence of a sensory impression of causation. Hume's argument is important not just because of its historical significance in the debate about the observability of causation, but because it raises issues that arise within that debate in a particularly pure form. The article considers several ways in which psychologists and philosophers have attempted to characterize the sense in which causation might be ‘observable’, and the implications for the viability of a regularity account of causation. It considers whether causation can be experienced in non-visual cases, specifically the experience of touch and the experience of agency. It also considers briefly whether the observability of causation makes trouble for broadly Humean, non-regularity accounts of causation, namely counterfactual, projectivist, and agency theories of causation.

When considering the relations between causation and reduction one must distinguish between, on the one hand, issues about how causation operates within and between systems that stand in various reductive relations to one another; and on the other hand, issues concerning whether causation itself is amenable to a reductive treatment. These two issues are intertwined and each must be treated with sympathy for the other. There are two basic types of reduction. Ontological reduction concerns reductive relations between the objects themselves whereas linguistic or conceptual reduction deals with reductive relations between our representations of those objects. For the great majority of the last century, both causation and reduction were treated linguistically or conceptually, but in recent years there has been a significant shift towards directly ontological treatments of each.

In the applied statistical literature, causal relations are often described equivocally or euphemistically as ‘risk factors’, or as part of ‘dimension reduction’. The statistical literature also tends to speak of ‘statistical models’ rather than of causal explanations, and to say that parameters of a model are ‘interpretable’, often means that the parameters make sense as measures of causal influence. These ellipses are due in part to the use of statistical formalisms for which a causal interpretation is wanted but unavailable or unfamiliar, and in part to a philosophical distrust of attributions of causation outside experimental contexts, misgivings traceable to the disciplinary institutionalization of claims of influential statisticians, notably Karl Pearson and Ronald Fisher. More candid treatments of causal relations have recently emerged in the theoretical statistical literature.