Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Structure-based variable selection for survival data
Bioinformatics
Causal discovery from a mixture of experimental and observational data
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
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We propose a novel approach for learning graphical models when data coming from different experimental conditions are available. We argue that classical constraint---based algorithms can be easily applied to mixture of experimental data given an appropriate conditional independence test. We show that, when perfect statistical inference are assumed, a sound conditional independence test for mixtures of experimental data can consist in evaluating the null hypothesis of conditional independence separately for each experimental condition. We successively indicate how this test can be modified in order to take in account statistical errors. Finally, we provide "Proof-of-Concept" results for demonstrating the validity of our claims.