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COGSCI
2010
107views more  COGSCI 2010»
14 years 9 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...
90
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IJAR
2008
155views more  IJAR 2008»
14 years 9 months ago
Estimation of causal effects using linear non-Gaussian causal models with hidden variables
The task of estimating causal effects from non-experimental data is notoriously difficult and unreliable. Nevertheless, precisely such estimates are commonly required in many fiel...
Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen...
ICML
2010
IEEE
14 years 10 months ago
Learning Temporal Causal Graphs for Relational Time-Series Analysis
Learning temporal causal graph structures from multivariate time-series data reveals important dependency relationships between current observations and histories, and provides a ...
Yan Liu 0002, Alexandru Niculescu-Mizil, Aurelie C...
IJCAI
1989
14 years 10 months ago
Reasoning About Hidden Mechanisms
1 describe an approach to the problem of forming hypotheses about hidden mechanisms w; thin devices — the "black box" problem for physical systems. The approach involv...
Richard J. Doyle
CORR
2007
Springer
109views Education» more  CORR 2007»
14 years 9 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...