C. Glymour

Publications363
h-index52
Citations15,928
Highly Influential Citations1,173
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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 our…
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 designed…
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 our…
A theory of causal learning in children: causal maps and Bayes nets.
TLDR
Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.
Causation, Prediction, and Search, Second Edition
An Algorithm for Fast Recovery of Sparse Causal Graphs
Previous asymptotically correct algorithms for recovering causal structure from sample probabilities have been limited even in sparse causal graphs to a few variables. We describe an asymptotically…
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 how…
Six problems for causal inference from fMRI
TLDR
Combinations of procedures that under these conditions find feed-forward sub-structure characteristic of a group of subjects are described.
Domain Adaptation with Conditional Transferable Components
TLDR
This paper aims to extract conditional transferable components whose conditional distribution is invariant after proper location-scale (LS) transformations, and identifies how P(Y) changes between domains simultaneously.
A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images
TLDR
A modification of the Greedy Equivalence Search algorithm to rapidly find the Markov Blanket of any variable in a high dimensional system is described.
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