Feature Selection and Causality Inference

Lecture slides:

• Feature Selection and Causality Inference
• Purpose
• Road Map
• Feature Selection
• Uncovering Dependencies
• Predictions and Actions
• Individual Feature Irrelevance pt 1
• Individual Feature Relevance pt 2
• Multivariate Cases
• Is multivariate FS always best?
• In practice…
• Definition of “relevance”
• Is X2 “relevant”?
• Are X1 and X2“relevant”?
• Adding a variable…
• X1 || Y | X2
• Really?
• Same independence relations Different causal relations
• Is X1 “relevant”?
• Non-causal features may be predictive yet not “relevant”
• Causal Features
• Experiments
• Univariate Filter: AUC
• Causal Feature Selection
• Causal features are “robust” under change of distribution
• Conclusion
• http://clopinet.com/fextract-book

Author: Isabelle Guyon, Clopinet