Causal Feature Selection: Uncover Relationships for Lung Cancer Prediction

1. Lecture 3:Causality and Feature Selection Isabelle Guyon isabelle@clopinet.com

2. Variable/feature selection Y X Remove features Xi to improve (or least degrade) prediction of Y.

3. What can go wrong? Guyon-Aliferis-Elisseeff, 2007

4. What can go wrong?

5. X2 Y Y X2 X1 X1 What can go wrong? Guyon-Aliferis-Elisseeff, 2007

6. Y Y X Causal feature selection Uncover causal relationships between Xi and Y.

7. Causal feature relevance Lung cancer

8. Causal feature relevance Lung cancer

9. Causal feature relevance Lung cancer

10. Markov Blanket Lung cancer Strongly relevant features (Kohavi-John, 1997) Markov Blanket (Tsamardinos-Aliferis, 2003)

11. Feature relevance • Surely irrelevant feature Xi: P(Xi, Y |S\i) = P(Xi |S\i)P(Y |S\i) for all S\i X\i and all assignment of values to S\i • Strongly relevant feature Xi: P(Xi, Y |X\i)  P(Xi |X\i)P(Y |X\i) for some assignment of values to X\i • Weakly relevant feature Xi: P(Xi, Y |S\i)  P(Xi |S\i)P(Y |S\i) for some assignment of values to S\i X\i

12. Markov Blanket Lung cancer Strongly relevant features (Kohavi-John, 1997) Markov Blanket (Tsamardinos-Aliferis, 2003)

13. Markov Blanket PARENTS Lung cancer Strongly relevant features (Kohavi-John, 1997) Markov Blanket (Tsamardinos-Aliferis, 2003)

14. Markov Blanket Lung cancer CHILDREN Strongly relevant features (Kohavi-John, 1997) Markov Blanket (Tsamardinos-Aliferis, 2003)

15. Markov Blanket SPOUSES Lung cancer Strongly relevant features (Kohavi-John, 1997) Markov Blanket (Tsamardinos-Aliferis, 2003)

16. Causal relevance • Surely irrelevant feature Xi: P(Xi, Y |S\i) = P(Xi |S\i)P(Y |S\i) for all S\i X\i and all assignment of values to S\i • Causally relevant feature Xi: P(Xi,Y|do(S\i))  P(Xi |do(S\i))P(Y|do(S\i)) for some assignment of values to S\i • Weak/strong causal relevance: • Weak=ancestors, indirect causes • Strong=parents, direct causes.

17. Examples Lung cancer

18. Immediate causes (parents) Genetic factor1 Smoking Lung cancer

19. Non-immediate causes (other ancestors) Smoking Anxiety Lung cancer

20. Non causes (e.g. siblings) Genetic factor1 Other cancers Lung cancer

21. X || Y | C CHAIN FORK C C X X Y

22. Hidden more direct cause Smoking Tar in lungs Anxiety Lung cancer

23. Confounder Smoking Genetic factor2 Lung cancer

24. Immediate consequences (children) Lung cancer Metastasis Coughing Biomarker1

25. X || Y but X || Y | C Lung cancer X X C C C X Strongly relevant features (Kohavi-John, 1997) Markov Blanket (Tsamardinos-Aliferis, 2003)

26. Non relevant spouse (artifact) Lung cancer Bio-marker2 Biomarker1

27. Systematic noise Another case of confounder Lung cancer Bio-marker2 Biomarker1

28. Truly relevant spouse Lung cancer Allergy Coughing

29. Sampling bias Lung cancer Metastasis Hormonal factor

30. Tar in lungs Genetic factor2 Bio-marker2 Allergy Systematic noise Biomarker1 Metastasis Coughing Hormonal factor Causal feature relevance Genetic factor1 Smoking Other cancers Anxiety Lung cancer (b)

31. Conclusion • Feature selection focuses on uncovering subsets of variables X1, X2, …predictive of the target Y. • Multivariate feature selection is in principle more powerful than univariate feature selection, but not always in practice. • Taking a closer look at the type of dependencies in terms of causal relationships may help refining the notion of variable relevance.

32. Acknowledgements and references • Feature Extraction, • Foundations and Applications • I. Guyon et al, Eds. • Springer, 2006. • http://clopinet.com/fextract-book • 2) Causal feature selection • I. Guyon, C. Aliferis, A. Elisseeff • In “Computational Methods of Feature Selection”, • Huan Liu and Hiroshi Motoda Eds., • Chapman and Hall/CRC Press, 2007. • http://clopinet.com/isabelle/Papers/causalFS.pdf