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

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

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

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). Causality Workbenchclopinet.com/causality

5. Causality Workbenchclopinet.com/causality Lung Cancer SmokingGenetics Coughing Attention Disorder Allergy AnxietyPeer Pressure Yellow Fingers Car Accident Born an Even Day Fatigue Causal graph example

6. Causality Workbenchclopinet.com/causality Lung Cancer SmokingGenetics Coughing Attention Disorder Allergy AnxietyPeer Pressure Yellow Fingers Car Accident Born an Even Day Fatigue Causality assessment with experiments

7. Causality Workbenchclopinet.com/causality Causality assessment without 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)

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

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

10. Scoring Causality Workbenchclopinet.com/causality S 0 A -> B A <- B A – B or 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

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

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

13. Causality Workbenchclopinet.com/causality A B A | B A ? B

14. Causality Workbenchclopinet.com/causality A B A - B A ? B

15. Causality Workbenchclopinet.com/causality 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!

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) Causality Workbenchclopinet.com/causality