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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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  • Likelihood and Consilience: On Forster’s Counterexamples to the Likelihood Theory of Evidence.Jiji Zhang & Kun Zhang - 2015 - Philosophy of Science 82 (5):930-940.
    Forster presented some interesting examples having to do with distinguishing the direction of causal influence between two variables, which he argued are counterexamples to the likelihood theory of evidence. In this paper, we refute Forster's arguments by carefully examining one of the alleged counterexamples. We argue that the example is not convincing as it relies on dubious intuitions that likelihoodists have forcefully criticized. More importantly, we show that contrary to Forster's contention, the consilience-based methodology he favored is accountable within the (...)
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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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