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  1. Situando Aristóteles na Discussão Acerca da Natureza da Causação.Davi Heckert César Bastos - 2018 - Dissertation, University of Campinas, Brazil
    I present Aristotle’s theory of causation in a way that privileges a comparison with contemporary discussion on causation. I do so by selecting in Aristotle’s theory points that are interesting to contemporary discussion and by translating Aristotle in the contemporary philosophical terminology. I compare Aristotle’s views with Mackie’s (1993/1965) and Sosa’s (1993/1980). Mackie is a humean regularist regarding the metaphysics of causal necessity, but his theory postulates some formal aspects of the causal relation which are similar to the Aristotelian theory. (...)
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  • Beyond Falsifiability: Normal Science in a Multiverse.Sean M. Carroll - 2019 - In Dawid Richard, Dardashti Radin & Thebault Karim (eds.), Epistemology of Fundamental Physics: Why Trust a Theory? Cambridge University Press.
    Cosmological models that invoke a multiverse - a collection of unobservable regions of space where conditions are very different from the region around us - are controversial, on the grounds that unobservable phenomena shouldn't play a crucial role in legitimate scientific theories. I argue that the way we evaluate multiverse models is precisely the same as the way we evaluate any other models, on the basis of abduction, Bayesian inference, and empirical success. There is no scientifically respectable way to do (...)
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  • Can Robots Do Epidemiology? Machine Learning, Causal Inference, and Predicting the Outcomes of Public Health Interventions.Alex Broadbent & Thomas Grote - 2022 - Philosophy and Technology 35 (1):1-22.
    This paper argues that machine learning and epidemiology are on collision course over causation. The discipline of epidemiology lays great emphasis on causation, while ML research does not. Some epidemiologists have proposed imposing what amounts to a causal constraint on ML in epidemiology, requiring it either to engage in causal inference or restrict itself to mere projection. We whittle down the issues to the question of whether causal knowledge is necessary for underwriting predictions about the outcomes of public health interventions. (...)
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