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34Our primary interest is in determining how many gene perturbation experiments are required to determine the Various algorithms have been proposed for learning..
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25We outline a cognitive and computational account of causal learning in children. We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent representation of the causal relations among events. This kind of knowledge can be perspicuously represented by the formalism of directed graphical causal models, or “Bayes nets”. Human causal learning and inference may involve computations similar to those for learnig…Read more
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29We describe a unification of old and recent ideas for formulating graphical models to explain time series data, including Granger causality, semi-automated search procedures for graphical causal models, modeling of contemporaneous influences in times series, and heuristic generalized additive model corrections to linear models. We illustrate the procedures by finding a structure of exogenous variables and mediating variables among time series of remote geospatial indices of ocean surface tempera…Read more
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155Instrumental ProbabilityThe Monist 84 (2): 284-300. 2001.The claims of science and the claims of probability combine in two ways. In one, probability is part of the content of science, as in statistical mechanics and quantum theory and an enormous range of "models" developed in applied statistics. In the other, probability is the tool used to explain and to justify methods of inference from records of observations, as in every science from psychiatry to physics. These intimacies between science and probability are logical sports, for while we think sc…Read more
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21Review of Eric Christian Barnes, The Paradox of Predictivism (review)Notre Dame Philosophical Reviews 2008 (6). 2008.
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115When is a brain like the planet?Philosophy of Science 74 (3): 330-347. 2007.Time series of macroscopic quantities that are aggregates of microscopic quantities, with unknown one‐many relations between macroscopic and microscopic states, are common in applied sciences, from economics to climate studies. When such time series of macroscopic quantities are claimed to be causal, the causal relations postulated are representable by a directed acyclic graph and associated probability distribution—sometimes called a dynamical Bayes net. Causal interpretations of such series im…Read more
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