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COGSCI
2010
107views more  COGSCI 2010»
13 years 5 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...
IJAR
2008
155views more  IJAR 2008»
13 years 4 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
13 years 5 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
13 years 6 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»
13 years 4 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...