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Causation, prediction, and search
What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and control ourExpand
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STATISTICS AND CAUSAL INFERENCE
Problems involving causal inference have dogged at the heels of Statistics since its earliest days. Correlation does not imply causation and yet causal conclusions drawn from a carefully designedExpand
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Causation, Prediction, and Search, 2nd Edition
What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and control ourExpand
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A theory of causal learning in children: causal maps and Bayes nets.
The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causalExpand
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An Algorithm for Fast Recovery of Sparse Causal Graphs
TLDR
We describe an asymptotically correct algorithm whose complexity for fixed graph connectivity increases polynomially in the number of vertices, and may in practice recover sparse graphs with several hundred variables. Expand
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Causation, Prediction, and Search, Second Edition
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The Mind's Arrows: Bayes Nets and Graphical Causal Models in Psychology
In recent years, small groups of statisticians, computer scientists, and philosophers have developed an account of how partial causal knowledge can be used to compute the effect of actions and howExpand
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Six problems for causal inference from fMRI
TLDR
We use the IMaGES algorithm to find feed-forward sub-structure characteristic of a group of subjects. Expand
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Domain Adaptation with Conditional Transferable Components
TLDR
Domain adaptation arises in supervised learning when the training (source domain) and test (target domain) data have different distributions. Expand
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Learning the Structure of Linear Latent Variable Models
TLDR
We introduce a novel algorithm for learning causal structure in linear models which, to the best of our knowledge, presents the first published solution for the problem of learning causal models with latent variables in a principled way where observed conditional independencies are not expected to exist. Expand
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