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1
Chapman, Harry. Nova Scotia's role in the War of 1812. Dartmouth, N.S: Dartmouth Historical Association, 2012.
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Plutynski, Anya. Causation, Causal Selection, and Causal Parity. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199967452.003.0004.
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It is typical to refer to cancer as a “genetic” or “genomic” disease. This claim is ambiguous; one of the central goals of this chapter is to disambiguate this claim. I first distinguish different types of causal claims: claims about causal relevance, causal role, and causal specificity. As a backdrop to this discussion, I introduce what I call the “mechanistic research program” in cancer, according to which progression to cancer involves breakdowns in regulatory controls on gene expression in ways that affect cell birth and death. While this research program has been successful, it has downplayed the role of context in cancer progression, and the fact that disorderly cellular growth is affected by many pathways. I conclude by considering several philosophers’ accounts of “causal selection” and argue that ultimately the causal selection problem is not one but several different problems, requiring different, context-specific solutions.
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Ahn, Woo-kyoung, Nancy S. Kim, and Matthew S. Lebowitz. The Role of Causal Knowledge in Reasoning About Mental Disorders. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.31.
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Despite the lack of scientific consensus about the etiologies of mental disorders, practicing clinicians and laypeople alike hold beliefs about the causes of mental disorders, and about the causal relations among symptoms and associated characteristics of mental disorders. This chapter summarizes research on how such causal knowledge systematically affects judgments about the diagnosis, prognosis, and treatment of mental disorders. During diagnosis, causal knowledge affects weighting of symptoms, perception of normality of behaviors, ascriptions of blame, and adherence to the DSM-based diagnostic categories. Regarding prognosis, attributing mental disorders to genetic or neurobiological abnormalities in particular engenders prognostic pessimism. Finally, both clinicians and laypeople endorse medication more strongly as an effective treatment if they believe mental disorders are biologically caused rather than psychologically caused. They also do so when considering disorders in the abstract versus equivalent concrete cases. The chapter discusses the rationality, potential mechanisms, and universality of these phenomena.
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Waldmann, Michael R. Causal Reasoning. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.1.
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Although causal reasoning is a component of most human cognitive functions, it has been neglected in cognitive psychology for many decades. To date, textbooks on cognitive psychology do not contain chapters on causal reasoning. The goal of this Handbook is to fill this gap, and to offer state-of-the-art reviews of the field. This introduction to the Handbook provides a general review of different competing theoretical frameworks modeling causal reasoning and learning. It outlines the relationship between psychological theories and their precursors in normative disciplines, such as philosophy and machine learning. It reviews the wide scope of tasks and domains in which the important role of causal knowledge has been documented. In the final section it previews the chapters of the handbook.
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Johnson, Samuel G. B., and Woo-kyoung Ahn. Causal Mechanisms. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.12.
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This chapter reviews empirical and theoretical results concerning knowledge of causal mechanisms—beliefs about how and why events are causally linked. First, it reviews the effects of mechanism knowledge, showing that mechanism knowledge can override other cues to causality (including covariation evidence and temporal cues) and structural constraints (the Markov condition), and that mechanisms play a key role in various forms of inductive inference. Second, it examines several theories of how mechanisms are mentally represented—as associations, forces or powers, icons, abstract placeholders, networks, or schemas—and the empirical evidence bearing on each theory. Finally, it describes ways that people acquire mechanism knowledge, discussing the contributions from statistical induction, testimony, reasoning, and perception. For each of these topics, it highlights key open questions for future research.
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McKitrick, Jennifer. Causal Relevance. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198717805.003.0010.
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A causally efficacious or relevant property is a property of a cause. However, not every property of a cause is causally relevant to its effect. Further conditions are needed to screen off causally irrelevant properties. Proposals for further conditions include: The causally relevant property must have explanatory power; there must be counterfactual dependence of the effect on the causally relevant property; there must be a lawful connection between the causally relevant property and its effect; the complete set of causally relevant properties must exclude any other properties from playing a causal role; the causally relevant property must be independent from its effect, in some sense; and finally, the causally relevant property is a member of a set of properties that is minimally sufficient for the effect. The most plausible accounts count dispositions as causally relevant.
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Lagerlund, Henrik, Benjamin Hill, and Stathis Psillos, eds. Reconsidering Causal Powers. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198869528.001.0001.
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Causal powers are returning to the forefront of realist philosophy of science. They were once central features of philosophical thinking about the natures of substances and causes but were banished during the early modern era and the Scientific Revolution. In this collection of essays, distinguished scholars revisit the fortunes of causal powers as scientific explanatory principles within the theories of substance and cause across history. Each author is focused on the philosophical role(s) causal powers was/were thought at the time to play and the reasons offered in support of, or against, their coherence and ability to perform their role(s). By placing rigorous philosophical analyses of thinking about causal powers within their historical contexts, features of their natures which might remain hidden to contemporary practitioners can be more readily identified and more carefully analysed. Canvassed are the thoughts of such important philosophers as Aristotle, Scotus, and Ockham and Buridan, then on through Suárez, Descartes, and Malebranche, to Locke and Hume, and ultimately to contemporary figures like the logical positivists, Goodman and Lewis.
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Rehder, Bob. Concepts as Causal Models. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.39.
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This chapter evaluates the case for treating concepts as causal models, the view that people conceive of a categories as consisting of not only features but also the causal relations that link those features. In particular, it reviews the role of causal models in categorization, the process of inferring an object’s category membership by observing its features. Reviewed studies include those testing categories that are either real world or artificial (made up of the experimenters) and subjects that are either adults or children. The chapter concludes that causal models provide accounts of causal-based categorization judgments that are superior to alternative accounts.
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Franklin, Christopher Evan. Minimal Event-Causal Libertarianism. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190682781.003.0002.
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This chapter explains the differences between agency reductionism and nonreductionism, explains the varieties of libertarianism, and sets out the main contours of minimal event-causal libertarianism, highlighting just how minimal this theory is. Crucial to understanding how minimal event-causal libertarianism differs from other event-causal libertarian theories is understanding the location and role of indeterminism in human action, the kinds of mental states essential to causing free action, the nature of nondeterministic causation, and how the theory is constructed from compatibilist accounts. The chapter argues that libertarians must face up to both the problem of luck and the problem of enhanced control when determining the best theoretical location of indeterminism.
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Rehder, Bob. Concepts as Causal Models. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.21.
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This chapter evaluates the case for treating concepts as causal models, the view that people conceive of a categories as consisting of not only features but also the causal relations that link those features. In particular, it reviews the role of causal models in category-based induction. Category-based induction consists of drawing inferences about either objects or categories; in the latter case one generalizes a feature to a category (and thus its members). How causal knowledge influences how categories are formed in the first place—causal-based category discovery—is also examined. Whereas the causal model approach provides a generally compelling account of a large variety of inductive inferences, certain key discrepancies between the theory and empirical findings are highlighted. The chapter concludes with a discussion of the new sorts of representations, tasks, and tests that should be applied to the causal model approach to concepts.
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Lee, Hyo-Dong. Ren and Causal Efficacy: Confucians and Whitehead on the Social Role of Symbolism. Edinburgh University Press, 2018. http://dx.doi.org/10.3366/edinburgh/9781474429566.003.0007.
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Confucians in East Asia have always dreamed of holding human communities together and constructing well-functioning polities in and through the binding and harmonizing power of rituals. Underlying their trust in the power of rituals is the notion that rituals constitute symbolic articulation and enchancement of our affective responses to the conditions of embodied relationality and historicity in which we always already find ourselves. This Confucian theory of rituals resonates with Whitehead’s theory of symbolism, insofar as the latter advances a primordially relational ontology of the subject by highlighting the hitherto neglected epistemological notion of perception in the mode of causal efficacy. As such, the Confucian theory of rituals offers a fresh cross-cultural perspective to understand Whitehead’s implied critique of the modern liberal social theories that are based on a view of human beings as atomized individuals who rationally consent to enter society.
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Cheng, Patricia W., and Hongjing Lu. Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.9.
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This chapter illustrates the representational nature of causal understanding of the world and examines its implications for causal learning. The vastness of the search space of causal relations, given the representational aspect of the problem, implies that powerful constraints are essential for arriving at adaptive causal relations. The chapter reviews (1) why causal invariance—the sameness of how a causal mechanism operates across contexts—is an essential constraint for causal learning in intuitive reasoning, (2) a psychological causal-learning theory that assumes causal invariance as a defeasible default, (3) some ways in which the computational role of causal invariance in causal learning can become obscured, and (4) the roles of causal invariance as a general aspiration, a default assumption, a criterion for hypothesis revision, and a domain-specific description. The chapter also reviews a puzzling discrepancy in the human and non-human causal and associative learning literatures and offers a potential explanation.
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Muentener, Paul, and Elizabeth Bonawitz. The Development of Causal Reasoning. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.40.
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Research on the development of causal reasoning has broadly focused on accomplishing two goals: understanding the origins of causal reasoning, and examining how causal reasoning changes with development. This chapter reviews evidence and theory that aim to fulfill both of these objectives. In the first section, it focuses on the research that explores the possible precedents for recognizing causal events in the world, reviewing evidence for three distinct mechanisms in early causal reasoning: physical launching events, agents and their actions, and covariation information. The second portion of the chapter examines the question of how older children learn about specific causal relationships. It focuses on the role of patterns of statistical evidence in guiding learning about causal structure, suggesting that even very young children leverage strong inductive biases with patterns of data to inform their inferences about causal events, and discussing ways in which children’s spontaneous play supports causal learning.
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Woodward, James. Some Varieties of Non-Causal Explanation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198777946.003.0007.
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This chapter explores the possibility of weakening the “interventionist” criteria for causal explanation described in Woodward’s Making Things Happen (2003) to yield various forms of non-causal explanation. These include the following: (1) retaining the idea that explanations must answer what-if-things-had-been-different questions (the w-question requirement), but dropping the requirement that the answers to such questions must take the form of claims about what would happen under interventions; (2) retaining the w-question requirement, but dropping the requirement that the generalization (if any) connecting explanans and explanandum be contingent and instead allowing generalizations that hold for mathematical or conceptual reasons to play this role; (3) dropping the w-question requirement to accommodate the role of information about irrelevance in explanation.
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Waldmann, Michael R., ed. The Oxford Handbook of Causal Reasoning. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.001.0001.
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Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive psychology. Despite the ubiquity of causal reasoning, textbooks of cognitive psychology have neglected this growing field. The goal of The Oxford Handbook of Causal Reasoning is to fill this gap. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields.
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Griffiths, Thomas L. Formalizing Prior Knowledge in Causal Induction. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.38.
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Prior knowledge plays a central role in causal induction, helping to explain how people are capable of identifying causal relationships from small amounts of data. Bayesian inference provides a way to characterize the influence that prior knowledge should have on causal induction, as well as an explanation for how that knowledge could itself be acquired. Using the theory-based causal induction framework of Griffiths and Tenenbaum (2009), this chapter reviews recent work exploring the relationship between prior knowledge and causal induction, highlighting some of the ways in which people’s expectations about causal relationships differ from approaches to causal learning in statistics and computer science.
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Franklin, Christopher Evan. The Limits of Event-Causal Libertarianism. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190682781.003.0008.
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This chapter probes the limits of event-causal libertarianism by assessing whether the assumption of agency reductionism is correct. It is argued that while Derk Pereboom’s Disappearing Agent Objection fails to refute minimal event-causal libertarianism, its key intuition can be recast in the more far-reaching It Ain’t Me Argument. Pereboom’s argument targets the libertarianism in event-causal libertarianism, whereas the It Ain’t Me Argument targets the agency reductionism in event-causal libertarianism, arguing that agency reductionism fails to afford the self the correct role in exercises of free will. The chapter closes by charting three possible reductionist responses to the It Ain’t Me Argument. It is argued that while it is not obvious that any of these responses fail, it is also not obvious that any succeeds, and thus the ultimate tenability of event-causal libertarianism is uncertain.
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Schloegl, Christian, and Julia Fischer. Causal Reasoning in Non-Human Animals. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.36.
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One goal of comparative cognitive studies is to achieve a better understanding of the selective pressures and constraints that play a role in cognitive evolution. This chapter focuses on the question of causal reasoning in animals, which has mainly been investigated in tool-using and large-brained species. Our survey reveals that numerous animal species appear to be sensitive to violations of causality and may even be tuned to attend to causally relevant features. This, in turn, may facilitate causal learning. The ability to draw logical conclusions and make causal deductions, however, seems to be restricted to few species and limited to (ecologically) relevant contexts. It seems warranted to reject the traditional associationist view that non-human animals lack any understanding of causality, but convincing evidence for human-like abilities is lacking. For instance, animals do not appear to understand the causal structure of interventions.
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Franklin, Christopher Evan. The Role and Location of Indeterminism. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190682781.003.0005.
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According to incompatibilists, free will and moral accountability exist only in nondeterministic worlds. But which ones? Where exactly must indeterminism be located, and what role must it play to make room for the possibility of freedom and accountability? This chapter evaluates three possible libertarian answers—non-action-centered accounts, nonbasic action-centered accounts, and basic action-centered accounts—and argues that libertarians should embrace a basic action-centered account that locates indeterminism at the moment of basic action (e.g., choice). Central to this chapter is showing that the source of the major problems with Kane’s event-causal libertarian theory can be traced to his problematic conception of the role and location of indeterminism and that we can avoid these problems by embracing the alternative conception developed in minimal event-causal libertarianism.
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Morrison, Margaret. The Non-Causal Character of Renormalization Group Explanations. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198777946.003.0011.
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After reviewing some of the recent literature on non-causal and mathematical explanation, this chapter develops an argument as to why renormalization group (RG) methods should be seen as providing non-causal, yet physical, information about certain kinds of systems/phenomena. The argument centres on the structural character of RG explanations and the relationship between RG and probability theory. These features are crucial for the claim that the non-causal status of RG explanations involves something different from simply ignoring or “averaging over” microphysical details—the kind of explanations common to statistical mechanics. The chapter concludes with a discussion of the role of RG in treating dynamical systems and how that role exemplifies the structural aspects of RG explanations which in turn exemplifies the non-causal features.
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Hart, Daniel, and Anne van Goethem. The Role of Civic and Political Participation in Successful Early Adulthood. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190260637.003.0012.
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Phenotypic positive youth civic development varies dramatically according to the political context in which it occurs. In democratic societies, successful individual development is reflected in commitment to and participation in existing civic structures. In contexts of oppression, however, positive youth civic development can include resistance and opposition. Research featuring designs that allow causal inferences is reviewed to identify developmental factors leading to positive youth civic development and political engagement. The impacts of family transitions, education, work, and community/national service on civic development are considered. We conclude with a plea for both the incorporation of meaning into accounts of positive youth development and more research allowing for causal inference concerning civic development.
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Hedström, Peter. Studying Mechanisms To Strengthen Causal Inferences In Quantitative Research. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0013.
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This article emphasizes various ways by which the study of mechanisms can make quantitative research more useful for causal inference. It concentrates on three aspects of the role of mechanisms in causal and statistical inference: how an understanding of the mechanisms at work can improve statistical inference by guiding the specification of the statistical models to be estimated; how mechanisms can strengthen causal inferences by improving our understanding of why individuals do what they do; and how mechanism-based models can strengthen causal inferences by showing why, acting as they do, individuals bring about the social outcomes they do. There has been a surge of interest in mechanism-based explanations, in political science as well as in sociology. Most of this work has been vital and valuable in that it has sought to clarify the distinctiveness of the approach and to apply it empirically.
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Freedman, David A. On Types of Scientific Enquiry: the Role of Qualitative Reasoning. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0012.
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This article argues that ‘substantial progress derives from informal reasoning and qualitative insights’. It shows the role played by causal process observations (CPOs), and qualitative reasoning more generally, in a series of well-known episodes drawn from the history of medicine. Edward Jenner published twenty-three case studies to demonstrate the safety and efficacy of ‘vaccination’. Ignac Semmelweis discovered the cause of puerperal fever. John Snow revealed that cholera was a water-borne infectious disease, which could be prevented by cleaning up the water supply. Christiaan Eijkman's research plan was to use Koch's methods, and show that beriberi was an infectious disease. Joseph Goldberger believed that pellagra was a deficiency disease. Frederick McKay and his connection with fluoridation, and the discovery of Alexander Fleming to penicillin, are discussed. In addition, the breakthrough of German measles by Norman Gregg is reported. Finally, Arthur Herbst offers the association between diethylstibestrol and adenocarcinoma of the vagina.
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Reiff, Mark R. Punishment in the executive suite: Moral responsibility, causal responsibility, and financial crime. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198755661.003.0006.
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Despite the enormity of the financial losses flowing from the 2008 financial crisis and the outrageousness of the conduct that led up to it, almost no individual involved has been prosecuted for criminal conduct, much less actually gone to prison. This chapter argues that the failure to punish those in management for their role in this misconduct stems from a misunderstanding of the need to prove that they personally knew of this wrongdoing and harbored an intent to defraud. Not only would negligence be a sufficient legal and moral basis for imposing terms of imprisonment in these cases, mere causal responsibility would also be enough, for causal responsibility has embedded in it all we need to find those causally responsible morally responsible too, and once some basis for moral responsibility is established, the imposition of terms of imprisonment is both legally possible and morally just.
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Cust, James. The Role of Governance and International Norms in Managing Natural Resources. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198817369.003.0019.
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The governance of natural resource wealth is considered to constitute a key determinant in whether the extraction of natural resources proves to be a blessing or a curse. In response to this challenge, a variety of international initiatives have emerged to codify successful policies pursued by countries, and promote global norms and best practices to guide decision-makers. These initiatives, such as the Extractives Industry Transparency Initiative, have seen success in spreading and embedding governance norms, ranging across revenue transparency, contract disclosure, and the creation of instruments such as resource funds and building institutions for checks and balances. However, evidence for causal impact remains weak and sometimes limited to anecdotal cases. The end of the super-cycle of commodity prices, and the prospect of permanently lower prices for fossil fuels, creates new challenges for resource-rich countries but may also allow space and time for reflection, lesson-learning and improvements in governance.
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Clatterbaugh, Kenneth. The Early Moderns. Edited by Helen Beebee, Christopher Hitchcock, and Peter Menzies. Oxford University Press, 2010. http://dx.doi.org/10.1093/oxfordhb/9780199279739.003.0004.
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The early moderns confronted an abundance of causes and types of causal explanations. From Aristotle through the Scholastics they had inherited the doctrine of the four causes, that is, depending upon context the material, formal, final, or efficient cause provides the proper explanation or answer to a ‘Why?’ question. From the Christianization of Aristotle's Unmoved Mover they had inherited the idea of God as creator of the universe. And to the creator role had been added the idea of providence whereby God in some sense ‘manages’ the world of mundane events. Aquinas had actually identified God as the efficient cause of all things.
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Hagmayer, York, and Philip Fernbach. Causality in Decision-Making. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.27.
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Although causality is rarely discussed in texts on decision-making, decisions often depend on causal knowledge and causal reasoning. This chapter reviews what is known about how people integrate causal considerations into their choice processes. It first introduces causal decision theory, a normative theory of choice based on the idea that rational decision-making requires considering the causal structure underlying a decision problem. It then provides an overview of empirical studies that explore how causal assumptions influence choice and test predictions derived from causal decision theory. Next it reviews three descriptive theories that integrate causal thinking into decision-making, each in a different way: the causal model theory of choice, the story model of decision-making, and attribution theory. It discusses commonalities and differences between the theories and the role of causality in other decision-making theories. It concludes by noting challenges that lie ahead for research on the role of causal reasoning in decision-making.
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Halpern, Joseph Y. Actual Causality. The MIT Press, 2017. http://dx.doi.org/10.7551/mitpress/9780262035026.001.0001.
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Causality plays a central role in the way people structure the world; we constantly seek causal explanations for our observations. But what does it even mean that an event C “actually caused” event E? The problem of defining actual causation goes beyond mere philosophical speculation. For example, in many legal arguments, it is precisely what needs to be established in order to determine responsibility. The philosophy literature has been struggling with the problem of defining causality since Hume. In this book, Joseph Halpern explores actual causality, and such related notions as degree of responsibility, degree of blame, and causal explanation. The goal is to arrive at a definition of causality that matches our natural language usage and is helpful, for example, to a jury deciding a legal case, a programmer looking for the line of code that cause some software to fail, or an economist trying to determine whether austerity caused a subsequent depression. Halpern applies and expands an approach to causality that he and Judea Pearl developed, based on structural equations. He carefully formulates a definition of causality, and building on this, defines degree of responsibility, degree of blame, and causal explanation. He concludes by discussing how these ideas can be applied to such practical problems as accountability and program verification.
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Woodward, James. Causation in Science. Edited by Paul Humphreys. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199368815.013.8.
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This article discusses some philosophical theories of causation and their application to several areas of science. Topics addressed include regularity, counterfactual, and causal process theories of causation; the causal interpretation of structural equation models and directed graphs; independence assumptions in causal reasoning; and the role of causal concepts in physics. In connection with this last topic, this article focuses on the relationship between causal asymmetries, the time-reversal invariance of most fundamental physical laws, and the significance of differences among varieties of differential equations (e.g., hyperbolic versus nonhyperbolic) in causal interpretation. It concludes with some remarks about “grounding” special science causal generalizations in physics.
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Woodward, James. Causation in Science. Edited by Paul Humphreys. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199368815.013.8_update_001.
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This article discusses some philosophical theories of causation and their application to several areas of science. Topics addressed include regularity, counterfactual, and causal process theories of causation; the causal interpretation of structural equation models and directed graphs; independence assumptions in causal reasoning; and the role of causal concepts in physics. In connection with this last topic, this article focuses on the relationship between causal asymmetries, the time-reversal invariance of most fundamental physical laws, and the significance of differences among varieties of differential equations (e.g., hyperbolic versus nonhyperbolic) in causal interpretation. It concludes with some remarks about “grounding” special science causal generalizations in physics.
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Franklin, Christopher Evan. A Minimal Libertarianism. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190682781.001.0001.
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In this book Franklin develops and defends a version of event-causal libertarianism about free will and moral responsibility. This view is a combination of libertarianism—the view that humans sometimes act freely and that those actions are the upshots of nondeterministic causal processes—and agency reductionism—the view that the causal role of agents in exercises of free will is exhausted by the causal role of mental states and events (e.g., desires and beliefs) involving the agents. Many philosophers contend that event-causal libertarians have no advantage over compatibilists when it comes to securing a distinctively valuable and robust kind of freedom and responsibility. But Franklin argues that this is mistaken. Assuming agency reductionism is true, event-causal libertarians need only adopt the most plausible compatibilist theory and add indeterminism at the proper juncture in the genesis of human action. The result is minimal event-causal libertarianism: a model of free will with the metaphysical simplicity of compatibilism and the intuitive power of libertarianism. And yet a worry remains. Toward the end of the book, Franklin reconsiders his assumption of agency reductionism, arguing that this picture faces a hitherto unsolved problem. This problem, however, has nothing to do with indeterminism or determinism, or even libertarianism or compatibilism, but with how to understand the nature of the self and its role in the genesis of action. If this problem proves unsolvable, then not only is event-causal libertarianism untenable, so also is event-causal compatibilism.
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Skow, Brad. Scientific Explanation. Edited by Paul Humphreys. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199368815.013.15.
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This chapter argues that the notion of explanation relevant to the philosophy of science is that of an answer to a why-question. From this point of view it surveys most of the historically important theories of explanation. Hempel’s deductive-nomological, and inductive-statistical, models of explanation required explanations to cite laws. Familiar counterexamples to these models suggested that laws are not needed, and instead that explanations should cite causes. One theory of causal explanation, David Lewis’s, is discussed in some detail. Many philosophers now reject causal theories of explanation because they think that there are non-causal explanations; some examples are reviewed. The role of probabilities and statistics in explanation, and their relation to causation, is also discussed. Another strategy for dealing with counterexamples to Hempel’s theory leads to unificationist theories of explanation. Kitcher's unificationist theory is presented, and a new argument against unificationist theories is offered. Also discussed in some detail are Van Fraassen’s pragmatic theory, and Streven’s and Woodward’s recent theories of causal explanation.
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Danks, David. Singular Causation. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.15.
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Causal relations between specific events are often critically important for learning, understanding, and reasoning about the world. This chapter examines both philosophical accounts of the nature of singular causation, and psychological theories of people’s judgments and reasoning about singular causation. It explores the content of different classes of theories, many of which are based on either some type of physical process connecting cause and effect, or else some kind of difference-making (or counterfactual) impact of the cause on the effect. In addition, this chapter examines various theoretical similarities and differences, particularly between philosophical and psychological theories that appear superficially similar. One consistent theme that emerges in almost every account is the role of general causal relations in shaping human judgments and understandings about singular causation.
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Birch, Jonathan. The Rule under Attack. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198733058.003.0003.
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HRG has been criticized for being an ‘empty statement’ or tautology, for failing to yield predictions, and for failing to yield causal explanations of change. There is some justification for these charges, yet they do not undermine the value of HRG as an organizing framework. In response to the ‘tautology’ complaint, we should admit that HRG is tautology-like, in that it avoids detailed dynamical assumptions. But this is an advantage in an organizing framework, because it ensures its compatibility with a wide range of more detailed models. In response to the ‘prediction’ complaint, we should concede that HRG is not very useful for prediction, but the role of an organizing framework is not predictive. In response to the ‘causal explanation’ complaint, this chapter argues that HRG, by organizing our thinking about ultimate causes, generates understanding of those causes. It also compares favourably to other possible organizing frameworks.
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Ben-Menahem, Yemima. Causation in Science. Princeton University Press, 2018. http://dx.doi.org/10.23943/princeton/9780691174938.001.0001.
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This book explores the role of causal constraints in science, shifting our attention from causal relations between individual events—the focus of most philosophical treatments of causation—to a broad family of concepts and principles generating constraints on possible change. The book looks at determinism, locality, stability, symmetry principles, conservation laws, and the principle of least action—causal constraints that serve to distinguish events and processes that our best scientific theories mandate or allow from those they rule out. The book's approach reveals that causation is just as relevant to explaining why certain events fail to occur as it is to explaining events that do occur. It investigates the conceptual differences between, and interrelations of, members of the causal family, thereby clarifying problems at the heart of the philosophy of science. The book argues that the distinction between determinism and stability is pertinent to the philosophy of history and the foundations of statistical mechanics, and that the interplay of determinism and locality is crucial for understanding quantum mechanics. Providing a historical perspective, the book traces the causal constraints of contemporary science to traditional intuitions about causation, and demonstrates how the teleological appearance of some constraints is explained away in current scientific theories such as quantum mechanics. The book represents a bold challenge to both causal eliminativism and causal reductionism—the notions that causation has no place in science and that higher-level causal claims are reducible to the causal claims of fundamental physics.
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Anjum, Rani Lill, and Stephen Mumford. The Explanatory Power of Mechanisms. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198733669.003.0013.
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There are quantitative and qualitative approaches to discovering causes in science. Quantitative approaches involve numeric values. The search for mechanisms falls on the qualitative side where the concern is not just with the what causes what and how often, but also with the how and the why. There can be some cases of strong support for a causal hypothesis on the grounds of mechanistic knowledge alone rather than evidence of a frequency of occurrence or repetition. Some traditional conceptions of mechanism involve a necessitating role on the production of effects. Mechanisms need not play a necessitating role in causation, however, nor need they involve activity in an occurrent sense.
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Osman, Magda. Planning and Control. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.19.
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For the best part of 30 years, the most influential theoretical and empirical work examining control-based decision-making and planning behaviors has largely neglected the importance of causality. Causal relations are essential for capturing the structural relationship between events in the world and individuals as they coordinate their actions toward anticipating (planning) and then managing (control) those events. Causal representations are the mental form by which individuals are able to simulate future events resulting from actions aimed at reaching a goal (planning), and maintaining that goal (control). The aim of this chapter is to examine the unsung work in planning and control that brings the role of causal relations and causal representations into the fore, and to speculate what the future research horizons for both might look like.
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Dowe, Phil. Causal Process Theories. Edited by Helen Beebee, Christopher Hitchcock, and Peter Menzies. Oxford University Press, 2010. http://dx.doi.org/10.1093/oxfordhb/9780199279739.003.0011.
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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.
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Menzies, Peter. The Consequence Argument Disarmed. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198746911.003.0016.
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This chapter scrutinizes the Consequence Argument for the incompatibility of free will and determinism within an interventionist causal-modelling framework. Traditional discussions of the argument presuppose that causal reasoning concerns the temporal evolution of total states of the universe. By contrast, interventionism focuses on how local, small-scale systems evolve according to causal generalizations that fall short of being laws. It also assigns an important role to interventions: external influences on a system that disrupt the causal generalizations which apply to it. When the Consequence Argument is recast in interventionist terms, the argument can be seen to rely on a false premise. In particular, interventionism supports at most a qualified variant of determinism, which is insufficient for the Consequence Argument to go through. Finally, the chapter compares the present response to the argument with David Lewis’s local miracle compatibilism.
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Geerse, Daniël A., and Marcus J. Schultz. Disorders of phosphate in the critically ill. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0254.
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Phosphorus plays an important role in many cellular processes and hypophosphataemia can result from a number of causes. Critically-ill patients are at increased risk for developing hypophosphataemia due to the presence of multiple causal factors. Hypophosphataemia may lead to a multitude of symptoms and frequent monitoring of serum phosphate is advised in critically-ill patients is recommended and should be corrected in patients with associated symptoms. It is uncertain whether correction in apparently asymptomatic patients affects outcome, although treatment is generally recommended for severe hypophosphataemia. Multiple strategies of intravenous phosphate administration have been described, but it is unknown which strategy is superior. Hyperphosphataemia is most often caused by renal insufficiency, but can also be caused by increased intake or release from damaged cells. Symptoms are mainly associated with subsequent hypocalcaemia and correction of hyperphosphataemia can be achieved by minimizing the intake of phosphate, increasing renal phosphate excretion, and renal replacement therapy.
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Stewart, Alex G., Sam Ghebrehewet, and Richard Jarvis. Cancer and chronic disease clusters. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198745471.003.0017.
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Clusters of disease are often reported to health protection and a finger pointed at a nearby environmental hazard that is thought to be the cause. There is an expectation that the causal linkage will be clarified and action taken to alleviate the resulting anxiety and stop further ill health. Not all reported clusters are real, but all are worth some level of investigation to alleviate anxiety. However, investigating such clusters is not easy. Neither is investigating causal linkages to environmental issues. Using an example of childhood cancer and contaminated land, this chapter takes a stepwise, structured approach to the investigation, defining realistic outcomes and clear criteria to stop such an investigation. The vital role of a multi-agency incident team to integrate health studies and environmental investigations is explored. Readers will be able to undertake such investigations for themselves across a wide range of putative clusters of chronic diseases.
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McCleary, Richard, David McDowall, and Bradley J. Bartos. Internal Validity. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0007.
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Chapter 7 begins with an outline and description of five threats to internal validity common to time series designs: history, maturation, instrumentation, regression, and selection. Given the fundamental role of prediction in the modern scientific method, scientific hypotheses are necessarily causal. After an outline of the evolving definition of “causality” in the social sciences, contemporary Rubin causality or counterfactual causality is introduced. Under the assumption that subjects were randomly assigned to the treatment and control groups, Rubin’s causal model allows one to estimate the unobserved causal parameter from observed data. Control time series are chosen so as to render plausible threats to internal validity implausible. An appropriate control time series may not exist, however, an ideal time series may be possible to construct. Synthetic control group models construct a control time series that optimally recreates the treated unit’s preintervention trend using a combination of untreated donor pool units.
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Wilson, Mark. Believers in The Land of Glory. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803478.003.0006.
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We employ words like “cause” both to structure an investigative architecture and to register concrete physical data in light of that strategic background. As a result, “cause”’s referential significance becomes very complicated as the word progressively enters fresh patches of application. Jim Woodward’s studies have demonstrated the central role that considerations of manipulative control play in mapping out the contours of these strategic specializations. In contrast, analytic metaphysicians have attempted to carve out an a priori pre-science of causal necessity that falters through ignoring these developmental considerations. All in all, this essay presents a critique of necessitarian thinking not unlike that offered by Quine in his well-known “Two Dogmas of Empiricism.”
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Thun, Michael, Martha S. Linet, James R. Cerhan, Christopher A. Haiman, and David Schottenfeld, eds. Cancer Epidemiology and Prevention. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.001.0001.
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Since its initial publication in 1982, Cancer Epidemiology and Prevention has served as the premier reference work for both students and professionals working to understand the causes and prevention of cancer in humans. Now revised for the first time in more than a decade, this fourth edition provides an updated and comprehensive summary of the global patterns of cancer incidence and mortality, current understanding of the major causal determinants, and a rationale for preventive interventions. In this edition, special attention has been paid to molecular epidemiologic approaches that address the wider role of genetic predisposition and gene-environment interactions in cancer etiology and pathogenesis.
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Gershman, Samuel J. Reinforcement Learning and Causal Models. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.20.
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This chapter reviews the diverse roles that causal knowledge plays in reinforcement learning. The first half of the chapter contrasts a “model-free” system that learns to repeat actions that lead to reward with a “model-based” system that learns a probabilistic causal model of the environment, which it then uses to plan action sequences. Evidence suggests that these two systems coexist in the brain, both competing and cooperating with each other. The interplay of two systems allows the brain to negotiate a balance between cognitively cheap but inaccurate model-free algorithms and accurate but expensive model-based algorithms. The second half of the chapter reviews research on hidden state inference in reinforcement learning. The problem of inferring hidden states can be construed in terms of inferring the latent causes that give rise to sensory data and rewards. Because hidden state inference affects both model-based and model-free reinforcement learning, causal knowledge impinges upon both systems.
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Burchell, Ann, and Eduardo Franco1. The impact of immunization on cancer control: the example of HPV vaccination. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199550173.003.0006.
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Chapter 6 reviews briefly the role of infections as causal agents in cancer, describes anti-hepatitis B virus (HBV) immunization as the first cancer vaccine paradigm, and finally focuses on the latest paradigm of prophylactic vaccination against human papillomavirus (HPV) infection as the new front in cancer prevention.
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Franklin, Christopher Evan. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190682781.003.0001.
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This chapter lays out the book’s central question: Assuming agency reductionism—that is, the thesis that the causal role of the agent in all agential activities is reducible to the causal role of states and events involving the agent—is it possible to construct a defensible model of libertarianism? It is explained that most think the answer is negative and this is because they think libertarians must embrace some form of agent-causation in order to address the problems of luck and enhanced control. The thesis of the book is that these philosophers are mistaken: it is possible to construct a libertarian model of free will and moral responsibility within an agency reductionist framework that silences that central objections to libertarianism by simply taking the best compatibilist model of freedom and adding indeterminism in the right junctures of human agency. A brief summary of the chapters to follow is given.
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Klosko, George. The American Welfare State. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199973415.003.0002.
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Background on the American welfare state. What we mean by welfare states; early history of American welfare state; causal factors in regard to how it developed, and the American welfare state in comparative perspective. We also look at the role of political justification in the development of American welfare programs.
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Anderson, Barton L. A Layered Experience of Lightness and Color. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199794607.003.0037.
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One of the fundamental debates about our experience of lightness and color involves their representational format. Some theories assert that the visual system decomposes the input into a layered representation of separated causes, whereas other theories do not. This chapter presents a variety of phenomena that directly demonstrate that layered image decompositions can play a causal role in our experience of lightness and color and discusses the theoretical implications and unresolved issues that are raised by these effects. The issue of the relationship between transparency and occlusion is discussed, as is relevance of the transparency phenomena to the problem of lightness and color perception more generally, which is an ongoing research problem and unresolved issue.
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Cartwright, Nancy. Causality, Invariance, and Policy. Edited by Don Ross and Harold Kincaid. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780195189254.003.0015.
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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.