4.1 High-dimensional interventions and causality : some results and many unsolved problems (Peter Bühlmann)

Update: 2014-12-04

Description

Understanding cause-effect relationships between variables is of interest in many fields of science. To effectively address such questions, we need to look beyond the framework of variable selection or importance from models describing associations only. We will show how graphical modeling and intervention calculus can be used for quantifying intervention and causal effects, particularly for high-dimensional, sparse settings where the number of variables can greatly exceed sample size.

Comments

In Channel

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1.2 On the regularization of Sliced Inverse Regression (Stéphane Girard)

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1.3 Simultaneous Gaussian Model-Based Clustering for Samples of Multiple Origins (Christophe Biernacki)

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2.1 Mixed-Membership Stochastic Block-Models for Transactional Data (Hugh Chipman)

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2.2 Visualization of graphs by organized clustering : application to social and biological networks (Nathalie Villa-Vialaneix)

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2.3 A Mixture of Experts Latent Position Cluster Model for Social Network Data (Claire Gormley)

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3.2 Regularization Methods for Categorical Predictors (Gerhard Tutz)

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4.1 High-dimensional interventions and causality : some results and many unsolved problems (Peter Bühlmann)

2014-12-0401:00:38

4.2 Statistical analysis of bio-molecular data and combinatorial difficulties : two examples (Stéphane Robin)

2014-12-0451:37

4.3 Data-driven penalties for optimal calibration of learning algorithms (Sylvain Arlot)

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4.1 High-dimensional interventions and causality : some results and many unsolved problems (Peter Bühlmann)

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4.1 High-dimensional interventions and causality : some results and many unsolved problems (Peter Bühlmann)

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4.1 High-dimensional interventions and causality : some results and many unsolved problems (Peter Bühlmann)