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  1. Editors' Introduction.Marc Artiga & Javier González de Prado - 2022 - Theoria. An International Journal for Theory, History and Foundations of Science 37 (1).
    Editors' introduction to 'James Woodward: Causal and explanatory asymmetries'.
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    Running It Up the Flagpole to See If Anyone Salutes: A Response to Woodward on Causal and Explanatory Asymmetries.Katrina Elliott & Marc Lange - 2022 - Theoria. An International Journal for Theory, History and Foundations of Science 37 (1).
    Does smoke cause fire or does fire cause smoke? James Woodward’s “Flagpoles anyone? Causal and explanatory asymmetries” argues that various statistical independence relations not only help us to uncover the directions of causal and explanatory relations in our world, but also are the worldly basis of causal and explanatory directions. We raise questions about Woodward’s envisioned epistemology, but our primary focus is on his metaphysics. We argue that any alleged connection between statistical dependence and causal/explanatory direction is contingent, at best. (...)
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  3. Bi-Directionality and Time in Causal Relationships.Fernanda Samaniego - 2022 - Theoria. An International Journal for Theory, History and Foundations of Science 37 (1).
    This paper aims to provide an answer to James Woodward’s article “Flagpoles anyone? Causal and explanatory asymmetries”. It will be conjectured that, when causal directionality depends on the experimental design, it is because the variables involved are capable of producing changes in each other. This will be exemplified using the case of ideal gases as opposed to the flagpole-shadow scenario.
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    The Fate of Causal Structure Under Time Reversal.Porter Williams - 2022 - Theoria. An International Journal for Theory, History and Foundations of Science 37 (1).
    What happens to the causal structure of a world when time is reversed? At first glance it seems there are two possible answers: the causal relations are reversed, or they are not. I argue that neither of these answers is correct: we should either deny that time-reversed worlds have causal relations at all, or deny that causal concepts developed in the actual world are reliable guides to the causal structure of time-reversed worlds. The first option is motivated by the instability (...)
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  5. Responses.James Woodward - 2022 - Theoria. An International Journal for Theory, History and Foundations of Science 37 (1).
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    Flagpoles Anyone? Causal and Explanatory Asymmetries.James Woodward - 2022 - Theoria. An International Journal for Theory, History and Foundations of Science 37 (1).
    This paper discusses some procedures developed in recent work in machine learning for inferring causal direction from observational data. The role of independence and invariance assumptions is emphasized. Several familiar examples including Hempel’s flagpole problem are explored in the light of these ideas. The framework is then applied to problems having to do with explanatory direction in non-causal explanation.
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    On the Unity Between Observational and Experimental Causal Discovery.Jiji Zhang - 2022 - Theoria. An International Journal for Theory, History and Foundations of Science 37 (1).
    In “Flagpoles anyone? Causal and explanatory asymmetries”, James Woodward supplements his celebrated interventionist account of causation and explanation with a set of new ideas about causal and explanatory asymmetries, which he extracts from some cutting-edge methods for causal discovery from observational data. Among other things, Woodward draws interesting connections between observational causal discovery and interventionist themes that are inspired in the first place by experimental causal discovery, alluding to a sort of unity between observational and experimental causal discovery. In this (...)
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    Computational Causal Discovery: Advantages and Assumptions.Kun Zhang - 2022 - Theoria. An International Journal for Theory, History and Foundations of Science 37 (1).
    I would like to congratulate James Woodward for another landmark accomplishment, after publishing his Making things happen: A theory of causal explanation. Making things happen gives an elegant interventionist theory for understanding explanation and causation. The new contribution relies on that theory and further makes a big step towards empirical inference of causal relations from non-experimental data. In this paper, I will focus on some of the emerging computational methods for finding causal relations from non-experimental data and attempt to complement (...)
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