Feature Selection and Causality Inference

author: Isabelle Guyon, Clopinet

Categories

Top: Computer Science: Machine Learning: Preprocessing

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*Slides*
0:00	Feature Selection and Causality Inference
2:11	Purpose
3:28	Road Map
3:44	Feature Selection
4:15	Uncovering Dependencies
6:27	Predictions and Actions
10:11	Individual Feature Irrelevance pt 1
12:53	Individual Feature Relevance pt 2
16:49	Multivariate Cases
20:03	Is multivariate FS always best?
21:54	In practice…
23:15	Definition of “relevance”
24:28	Is X2 “relevant”?
29:54	Are X1 and X2“relevant”?
33:28	Adding a variable…
35:13	X1 \|\| Y \| X2
36:33	Really?
37:18	Same independence relations Different causal relations
39:11	Is X1 “relevant”?
40:08	Non-causal features may be predictive yet not “relevant”
43:52	Causal Features
47:06	Experiments
51:17	Univariate Filter: AUC
54:45	Causal Feature Selection
55:25	Causal features are “robust” under change of distribution
59:20	Conclusion
60:08	http://clopinet.com/fextract-book

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