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  1. 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):63-74.
    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):75-86.
    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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  • On Wolfgang Spohn’s Laws of Belief.James Woodward - 2019 - Philosophy of Science 86 (4):759-772.
    This is one of a pair of discussion notes comparing some features of the account of causation in Wolfgang Spohn’s Laws of Belief with the “interventionist” account in James Woodward’s Making Things Happen. Despite striking similarities there are also important differences. These include the “epistemic” orientation of Spohn’s account as opposed to the worldly or “ontic” orientation of the interventionist account, Spohn’s focus on token-level causal claims in contrast to the primary interventionist focus on type-level claims, the role of temporal (...)
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  • A Functional Account of Causation; or, A Defense of the Legitimacy of Causal Thinking by Reference to the Only Standard That Matters—Usefulness.James Woodward - 2014 - Philosophy of Science 81 (5):691-713.
    This essay advocates a “functional” approach to causation and causal reasoning: these are to be understood in terms of the goals and purposes of causal thinking. This approach is distinguished from accounts based on metaphysical considerations or on reconstruction of “intuitions.”.
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  • Entanglement, Complexity, and Causal Asymmetry in Quantum Theories.Porter Williams - 2022 - Foundations of Physics 52 (2):1-38.
    It is often claimed that one cannot locate a notion of causation in fundamental physical theories. The reason most commonly given is that the dynamics of those theories do not support any distinction between the past and the future, and this vitiates any attempt to locate a notion of causal asymmetry—and thus of causation—in fundamental physical theories. I argue that this is incorrect: the ubiquitous generation of entanglement between quantum systems grounds a relevant asymmetry in the dynamical evolution of quantum (...)
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  • Causal discovery algorithms: A practical guide.Daniel Malinsky & David Danks - 2018 - Philosophy Compass 13 (1):e12470.
    Many investigations into the world, including philosophical ones, aim to discover causal knowledge, and many experimental methods have been developed to assist in causal discovery. More recently, algorithms have emerged that can also learn causal structure from purely or mostly observational data, as well as experimental data. These methods have started to be applied in various philosophical contexts, such as debates about our concepts of free will and determinism. This paper provides a “user's guide” to these methods, though not in (...)
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  • Replacing Causal Faithfulness with Algorithmic Independence of Conditionals.Jan Lemeire & Dominik Janzing - 2013 - Minds and Machines 23 (2):227-249.
    Independence of Conditionals (IC) has recently been proposed as a basic rule for causal structure learning. If a Bayesian network represents the causal structure, its Conditional Probability Distributions (CPDs) should be algorithmically independent. In this paper we compare IC with causal faithfulness (FF), stating that only those conditional independences that are implied by the causal Markov condition hold true. The latter is a basic postulate in common approaches to causal structure learning. The common spirit of FF and IC is to (...)
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  • Discovering agents.Zachary Kenton, Ramana Kumar, Sebastian Farquhar, Jonathan Richens, Matt MacDermott & Tom Everitt - 2023 - Artificial Intelligence 322 (C):103963.
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  • Causal versions of maximum entropy and principle of insufficient reason.Dominik Janzing - 2021 - Journal of Causal Inference 9 (1):285-301.
    The principle of insufficient reason assigns equal probabilities to each alternative of a random experiment whenever there is no reason to prefer one over the other. The maximum entropy principle generalizes PIR to the case where statistical information like expectations are given. It is known that both principles result in paradoxical probability updates for joint distributions of cause and effect. This is because constraints on the conditional P P\left result in changes of P P\left that assign higher probability to those (...)
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