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COGSCI
2010
107views more  COGSCI 2010»
13 years 7 months ago
Inferring Hidden Causal Structure
We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a ...
Tamar Kushnir, Alison Gopnik, Chris Lucas, Laura S...
UAI
2001
13 years 8 months ago
A Calculus for Causal Relevance
We present a sound and complete calculus for causal relevance that uses Pearl's functional causal models as semantics. The calculus consists of axioms and rules of inference ...
Blai Bonet
CORR
2007
Springer
109views Education» more  CORR 2007»
13 years 7 months ago
Optimal Causal Inference
We consider an information-theoretic objective function for statistical modeling of time series that embodies a parametrized trade-off between the predictive power of a model and...
Susanne Still, James P. Crutchfield, Christopher J...
JMLR
2010
93views more  JMLR 2010»
13 years 2 months ago
Sufficient covariates and linear propensity analysis
Working within the decision-theoretic framework for causal inference, we study the properties of "sufficient covariates", which support causal inference from observation...
Hui Guo, A. Philip Dawid
JMLR
2010
134views more  JMLR 2010»
13 years 2 months ago
Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions
This paper studies the feasibility and interpretation of learning the causal structure from observational data with the principles behind the Kolmogorov Minimal Sufficient Statist...
Jan Lemeire, Kris Steenhaut