Causal Discovery Challenge: Exploring Cause-Effect Relationships for a Better Tomorrow

1. IJCNN 2013 IEEE/INNS Cause-Effect Pair Challenge Isabelle Guyon, ChaLearn clopinet.com/causality

2. …your health? …climate changes? … the economy? Causal discovery What affects… Which actions will have beneficial effects? clopinet.com/causality

3. Available data • A lot of “observational” data. Correlation  Causality! • Experiments are often needed, but: • Costly • Unethical • Infeasible clopinet.com/causality

4. Setup • No feed-back loops. • No time. Samples are drawn randomly and independently. We consider pairs of variables {A, B} for which A B means A = f (B, noise). clopinet.com/causality

5. Anxiety Peer Pressure Born an Even Day Yellow Fingers Smoking Genetics Allergy Lung Cancer Attention Disorder Coughing Fatigue Car Accident Causal graph example clopinet.com/causality

6. Anxiety Peer Pressure Born an Even Day Yellow Fingers Smoking Genetics Allergy Lung Cancer Attention Disorder Coughing Fatigue Car Accident Causality assessmentwith experiments clopinet.com/causality

7. Causality assessmentwithout experiments? • Possible to some extent, using: • Conditional independence tests, e.g. in A -> Z -> B, A <- Z <- B or A <- Z -> B, • A is independent of B given Z • but NOT in A -> Z <- B • But… • Such methods require a lot of data to work well and often rely on simplifying assumptions (e.g. “causal sufficiency”, “faithfulness”, linearity, Gaussian noise) clopinet.com/causality

8. Cause-effect pair problem A B Smoking Lung Cancer Lung Cancer Fatigue A -> B A <- B A – B A | B Genetics Attention Disorder Lung Cancer Born an Even Day Lung Cancer clopinet.com/causality

9. Typical method Test whether A -> B is a better explanation than A <- B comparing two models: B = f (A, noise) A = f (B, noise) clopinet.com/causality

10. Scoring S 0 A -> B A – B or A|B A <- B • Is A a cause of B, B a cause of A, or neither? • Average two AUCs for the separations: • A -> B vs. A – B, A | B, A <- B • A <- B vs. A – B, A | B, A -> B clopinet.com/causality

11. A ? B A -> B B =Altitude B A A = Temperature clopinet.com/causality

12. A ? B A <- B B =Wages B A A = Age clopinet.com/causality

13. A ? B A | B B A clopinet.com/causality

14. A ? B A - B B A clopinet.com/causality

15. Conclusion • Imagine…that we could find out: • what causes epidemics • what causes cancer • what causes climate changes • what causes economic changes by analyzing data constantly collected • Bring your solution or your own data! clopinet.com/causality

16. Credits • Initial impulse: the cause-effect pair task proposed in the causality "pot-luck" challenge by Joris Mooij, Dominik Janzing, and Bernhard Schölkopf. • Protocol review, advisors and beta testers • Hugo Jair Escalante (IANOE, Mexico) 
 • Seth Flaxman (Carnegie Mellon University, USA)
 • Mikael Henaff (New York University, USA) 
 • Dominik Janzing (Max Plank Institute of Biological cybernetics, Germany) 
 • Florin Popescu (Fraunhofer Institute, Berlin, Germany) 
 • Bernhard Schoelkopf (Max Plank Institute of Biological cybernetics, Germany) 
 • Peter Spirtes (Carnegie Mellon University, USA) 
 • Alexander Statnikov (New York University, USA) 
 • Ioannis Tsamardinos (University of Crete, Greece) 
 • Jianxin Yin (University of Pennsylvannia, USA) 
 • Kun Zhang (Max Plank Institute of Biological cybernetics, Germany) • Vincent Lemaire (Orange, France) • Data and code preparation • Isabelle Guyon (ChaLearn, USA) • Alexander Statnikov (New York University, USA) • Mikael Henaff (New York University, USA) clopinet.com/causality