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1
Long, Derek. Reasoning by analogy and causality: A model and application. New York: Ellis Horwood, 1994.
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2
Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Empirically Detecting Causality. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0008.
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Phenomenological models mathematically describe relationships among empirically observed phenomena without attempting to explain underlying mechanisms. Within the context of NLTS, phenomenological modeling goes beyond phase space reconstruction to extract equations governing real-world system dynamics from a single or multiple observed time series. Phenomenological models provide several benefits. They can be used to characterize the dynamics of variable interactions; for example, whether an incremental increase in one variable drives a marginal increase/decrease in the growth rate of another, and whether these dynamic interactions follow systematic patterns over time. They provide an analytical framework for data driven science still searching for credible theoretical explanation. They set a descriptive standard for how the real world operates so that theory is not misdirected in explaining fanciful behavior. The success of phenomenological modeling depends critically on selection of governing parameters. Model dimensionality, and the time delays used to synthesize dynamic variables, are guided by statistical tests run for phase space reconstruction. Other regression and numerical integration parameters can be set on a trial and error basis within ranges providing numerical stability and successful reproduction of empirically-detected dynamics. We illustrate phenomenological modeling with solutions of the Lorenz model so that we can recognize the dynamics that need to be reproduced.
3
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.
4
Malmgren, Helge. The theoretical basis of the biopsychosocial model. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198530343.003.0002.
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This chapter addresses the philosophy behind the biopsychosocial model. It summarizes five aetiological problems that the biopsychosocial model must address (nature versus nurture; single-factor versus multifactor causality; somatic versus mental causes; reasons versus causes; conscious versus non-conscious influences) with a particular focus on the mind-body problem, and uses an analogy between computer hardware and software to describe the relationship between the mind and body.
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Solstad, Torgrim, and Oliver Bott. Causality and Causal Reasoning in Natural Language. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.32.
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This chapter provides a combined overview of theoretical and psycholinguistic approaches to causality in language. The chapter’s main phenomenological focus is on causal relations as expressed intra-clausally by verbs (e.g., break, open) and between sentences by discourse markers (e.g., because, therefore). Special attention is given to implicit causality verbs that are argued to trigger expectations of explanations to occur in subsequent discourse. The chapter also discusses linguistic expressions that do not encode causation as such, but that seem to be dependent on a causal model for their adequate evaluation, such as counterfactual conditionals. The discussion of the phenomena is complemented by an overview of important aspects of their cognitive processing as revealed by psycholinguistic experimentation.
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Brady, Henry E. Causation and Explanation in Social Science. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0010.
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This article provides an overview of causal thinking by characterizing four approaches to causal inference. It also describes the INUS model. It specifically presents a user-friendly synopsis of philosophical and statistical musings about causation. The four approaches to causality include neo-Humean regularity, counterfactual, manipulation and mechanisms, and capacities. A counterfactual is a statement, typically in the subjunctive mood, in which a false or ‘counter to fact’ premise is followed by some assertion about what would have happened if the premise were true. Three basic questions about causality are then addressed. Moreover, the article gives a review of four approaches of what causality might be. It pays attention on a counterfactual definition, mostly amounting to a recipe that is now widely used in statistics. It ends with a discussion of the limitations of the recipe and how far it goes toward solving the epistemological and ontological problems.
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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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Boland, Lawrence A. Equilibrium concepts and critiques. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190274320.003.0006.
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This chapter explores the equilibrium concept by examining the views of two cultures: those who began talking about equilibrium models in the decades before World War II and those formal model builders promoting mathematics after that war. For the older culture, the concept of an equilibrium refers to the real properties of an actual economy in a state of equilibrium. For the newer culture, an equilibrium refers only to a property of a formal mathematical model. The main discussion of the chapter is about the various critiques provided by both sides of the cultural divide. The chapter also discusses the extent to which the distinction between a model’s exogenous vs. endogenous variables involves causality. The older culture would view causality as a necessary part of understanding an equilibrium but the newer culture would view it only as an interpretation of the mathematics of the model.
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Wendling, Fabrice, Marco Congendo, and Fernando H. Lopes da Silva. EEG Analysis. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0044.
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This chapter addresses the analysis and quantification of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Topics include characteristics of these signals and practical issues such as sampling, filtering, and artifact rejection. Basic concepts of analysis in time and frequency domains are presented, with attention to non-stationary signals focusing on time-frequency signal decomposition, analytic signal and Hilbert transform, wavelet transform, matching pursuit, blind source separation and independent component analysis, canonical correlation analysis, and empirical model decomposition. The behavior of these methods in denoising EEG signals is illustrated. Concepts of functional and effective connectivity are developed with emphasis on methods to estimate causality and phase and time delays using linear and nonlinear methods. Attention is given to Granger causality and methods inspired by this concept. A concrete example is provided to show how information processing methods can be combined in the detection and classification of transient events in EEG/MEG signals.
10
Barham, Jeremy. Mahler and the Game of History. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199316090.003.0017.
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For obvious reasons, the understanding and writing of music history have favoured a linear model founded in causality and chronology. Like many disciplines, however, historiographical studies have been subjected to critiques of various theoretical and imaginative types, particularly, but not exclusively, in recent times. These critiques are outlined here, and three historiographical models critically applied to the understanding of Mahler’s music: historicism, historical materialism (after Walter Benjamin), and a more radical rhizomatic model (after Deleuze). Posited, put into operation and questioned, these models cast multi-perspectival and multi-temporal light on how Mahler’s music continues to participate in contexts of contemporary mass-media and public consciousness.
11
Okasha, Samir. 3. Explanation in science. Oxford University Press, 2013. http://dx.doi.org/10.1093/actrade/9780192802835.003.0003.
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What exactly is scientific explanation? ‘Explanation in science’ begins with Carl Hempel's covering law model of explanation, which says that to explain a phenomenon is to show that its occurrence follows deductively from a general law, perhaps supplemented by other laws and/or particular facts, all of which must be true. This model does not deal with symmetry or irrelevance. The covering law model implies that explanation should be a symmetric relation, but in fact it is asymmetric. Also, a good explanation of a phenomenon should contain information that is relevant to the phenomenon's occurrence. Causality-based accounts of scientific explanation and the concepts of reduction and multiple realization are also explained.
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Bayer, Stefan, Kirsten Dickhaut, and Irene Herzog, eds. Lenkung der Dinge. Klostermann, 2021. http://dx.doi.org/10.5771/9783465145585.
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In the course of the humanistic examination of his position in the cosmos, man in the early modern period also reformulates his radius of action: the causality model of the 'steering of things', which is rooted in a hierarchical structure at the top of which magicians, political rulers or princes, and artists appear as sovereigns of action, describes the possibilities of successful and effective action in magic, politics, and art. The question discussed in literary texts, in the arts, and in treatises on statecraft in the early modern period is the possibility and nature of the controllability of external as well as internal nature. The contributions to this volume discuss the concept of the "steering of things" against the backdrop of its historical, cultural and epistemological context.
13
Weiss, Alexander, and Marieke Gartner. Animal Personality. Edited by Thomas A. Widiger. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199352487.013.24.
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Animal personality has been studied for decades, and a recent renaissance in the field has revealed links to health and life outcomes that echo those found in humans. Some of this research is tied to the Five Factor Model—the predominant model of human personality—which informs animal personality research as well, and allows for comparative work that points to evolutionary pathways that delineate phylogenetic continuity. From personality facets and traits to factors, this work has implications for human and nonhuman animal genetics, life history strategies, survival, and well-being, as well as development and social relationships. Working together, scientists from a variety of fields who study personality can hope to puzzle out causality, use personality as a tool for health, and simply define personality, across species, and therefore evolutionary time.
14
Goodman, Steven N., and Jonathan M. Samet. Causal Inference in Cancer Epidemiology. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0007.
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Judgments about causality are central to the development of interventions intended to reduce exposure to risk factors that cause cancer. Because causation is not directly observable in medicine, scientists and philosophers have had to develop sets of constructs and heuristics that define “cause” operationally. The criteria in this framework, often attributed to the British medical statistician Sir Austin Bradford Hill or to the 1964 Report of the US Surgeon General on tobacco, include consistency, strength of association, specificity, temporality, coherence/plausibility/analogy, biological gradient, and experiment. This chapter reviews these criteria in depth and considers the challenges of applying them to population research on cancer. It discusses the concepts of causation in the context of the multistage nature of cancer, the “counterfactual” notion of causation, the component cause model for understanding diseases with multiple causes, and the “weight of the evidence” approach for integrating information from multiple lines of research.
15
Humphreys, Paul. Philosophical Papers. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780199334872.001.0001.
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Paul Humphreys pioneered philosophical investigations into the methodological revolution begun by computer simulations. He has also made important contributions to the contemporary literature on emergence by developing the fusion account of diachronic emergence and its generalization, transformational emergence. He is the discoverer of what has come to be called “Humphreys” Paradox in probability theory and has also made influential contributions to the literature on probabilistic causality and scientific explanation. This collection contains fourteen of his previously published papers on topics ranging from numerical experiments to the status of scientific metaphysics. There is also and a previously unpublished paper on social dynamics. The volume is divided into four parts on, respectively, computational science, emergence, probability, and general philosophy of science. The first part contains the seminal 1990 paper on computer simulations, with three other papers arguing that these new methods cannot be accounted for by traditional methodological approaches. The second part contains the original presentation of fusion emergence and three companion papers arguing for diachronic approaches to the topic, rather than the then dominant synchronic accounts. The third part starts with the paper that introduced the probabilistic paradox followed by a later evaluation of attempts to solve it. A third paper argues, contra Quine, that probability theory is a purely mathematical theory. The final part includes papers on causation, explanation, metaphysics, and an agent-based model that shows how endogenous uncertainty undermines utility maximization. Each of the four parts is followed by a comprehensive postscript with retrospective assessments.
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Baulieu, Laurent, John Iliopoulos, and Roland Sénéor. From Classical to Quantum Fields. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198788393.001.0001.
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Quantum field theory has become the universal language of most modern theoretical physics. This book is meant to provide an introduction to this subject with particular emphasis on the physics of the fundamental interactions and elementary particles. It is addressed to advanced undergraduate, or beginning graduate, students, who have majored in physics or mathematics. The ambition is to show how these two disciplines, through their mutual interactions over the past hundred years, have enriched themselves and have both shaped our understanding of the fundamental laws of nature. The subject of this book, the transition from a classical field theory to the corresponding Quantum Field Theory through the use of Feynman’s functional integral, perfectly exemplifies this connection. It is shown how some fundamental physical principles, such as relativistic invariance, locality of the interactions, causality and positivity of the energy, form the basic elements of a modern physical theory. The standard theory of the fundamental forces is a perfect example of this connection. Based on some abstract concepts, such as group theory, gauge symmetries, and differential geometry, it provides for a detailed model whose agreement with experiment has been spectacular. The book starts with a brief description of the field theory axioms and explains the principles of gauge invariance and spontaneous symmetry breaking. It develops the techniques of perturbation theory and renormalisation with some specific examples. The last Chapters contain a presentation of the standard model and its experimental successes, as well as the attempts to go beyond with a discussion of grand unified theories and supersymmetry.