Challenges in Causality

1. Challenges in Causality Isabelle Guyon, Clopinet Constantin Aliferis and Alexander Statnikov, Vanderbilt Univ. André Elisseeff and Jean-Philippe Pellet, IBM Zürich Gregory F. Cooper, Pittsburg University Peter Spirtes, Carnegie Mellon clopinet.com/causality

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

3. What is causality? • Many definitions: • Science • Philosophy • Law • Psychology • History • Religion • Engineering • “Cause is the effect concealed, effect is the cause revealed”(Hindu philosophy) clopinet.com/causality

4. An engineering view… clopinet.com/causality

5. The system External agent Systemic causality clopinet.com/causality

6. Feature Selection Y X Predict Y from features X1, X2, … Select most predictive features. clopinet.com/causality

7. Y Y X Causation Predict the consequences of actions: Under “manipulations” by an external agent, some features are no longer predictive. clopinet.com/causality

8. What is out there? clopinet.com/causality

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

10. Causal discovery from “observational data” Example algorithm: PC(Peter Spirtes and Clarck Glymour, 1999) Let A, B, C Xand V X. Initialize with a fully connected un-oriented graph. • Conditional independence. Cut connection if  Vs.t. (A  B |V). • Colliders. In triplets A —C —B (A — B) if there is no subset V containing C s.t. A  B |V, orient edgesas: A C B. • Constraint-propagation. Orient edges until no change: (i) If A B …C, and A —C then A C. (ii) If A B —C then B C. clopinet.com/causality

11. Difficulties • Violated assumptions: • Causal sufficiency • Markov equivalence • Faithfulness • Linearity • Gaussianity • Overfitting (statistical complexity): • Finite sample size • Algorithm efficiency (computational complexity): • Thousands of variables • Tens of thousands of examples clopinet.com/causality

12. Causality workbench clopinet.com/causality

13. Our approach What is the causal question? Why should we care? What is hard about it? Is this solvable? Is this a good benchmark? clopinet.com/causality

14. Challenge datasets Toy datasets First datasets clopinet.com/causality

15. On-line feed-back clopinet.com/causality

16. Our challenges Find… • Problems • Data • Metrics • Challenge protocols • Implementation clopinet.com/causality

17. Healthcare mass spec Marketing Ecology DALTON Conceptual ECONO Neuroscience Epidemiology Psychology TIED Climatology Internet Sociology Security Upcoming datasets clopinet.com/causality

18. Want to contribute data? • Real data: • Non confidential • Large number of samples • Large number of variables • Observational and experimental • Semi-artificial data: • Re-simulated • Real data + artificial variables clopinet.com/causality

19. Performance assessment clopinet.com/causality

20. Metrics • Fulfillment of an objective: • Future (prediction) • Past (counterfactual) • Causal relationships: • Existence • Strength • Degree clopinet.com/causality

21. Examples of objectives • Medicine and epidemiology • Maximize life expectancy • Maximize drug efficacy • Minimize contagion • Economy and marketing • Maximize Gross National Product (GNP) • Maximize sales • Minimize churn rate clopinet.com/causality

22. Anxiety Peer Pressure Born an Even Day Yellow Fingers Smoking Genetics Allergy Lung Cancer Attention Disorder Coughing Fatigue LUCAS0: natural Car Accident Causality assessmentwith manipulations clopinet.com/causality

23. Anxiety Peer Pressure Born an Even Day Yellow Fingers Smoking Genetics Allergy Lung Cancer Attention Disorder Coughing Fatigue Car Accident Causality assessmentwith manipulations LUCAS1: manipulated clopinet.com/causality

24. Anxiety Peer Pressure Born an Even Day Yellow Fingers Smoking Genetics Allergy Lung Cancer Attention Disorder Coughing Fatigue Car Accident Causality assessmentwith manipulations LUCAS2: manipulated clopinet.com/causality

25. 10 2 5 3 9 4 1 0 6 11 8 • Participants return: S=selected subset 7 11 4 1 2 3 (ordered or not). Goal driven causality • We define: • V=variables of interest • (e.g. MB, direct causes, ...) • We assess causal relevance: R=f(V,S). clopinet.com/causality

26. Causality assessmentwithout manipulation? clopinet.com/causality

27. P1 P2 P3 PT Probes Using artificial “probes” Anxiety Peer Pressure Born an Even Day Yellow Fingers Smoking Genetics Allergy Lung Cancer Attention Disorder LUCAP0: natural Coughing Fatigue Car Accident clopinet.com/causality

28. Anxiety Peer Pressure Born an Even Day Yellow Fingers Smoking Genetics Allergy Lung Cancer Attention Disorder Coughing Fatigue Car Accident P1 P2 P3 PT Probes Using artificial “probes” LUCAP1&2: manipulated clopinet.com/causality

29. Scoring using “probes” • What we can compute (Fscore): • Negative class = probes (here, all “non-causes”, all manipulated). • Positive class = other variables (may include causes and non causes). • What we want (Rscore): • Positive class = causes. • Negative class = non-causes. • What we get (asymptotically): Fscore = (NTruePos/NReal) Rscore + 0.5 (NTrueNeg/NReal) clopinet.com/causality

30. Conclusion • Try our first challenge, learn, and win!!!! • WCCI08 Workshop. Hong-Kong, June, 2008 • travel grants for top ranking students. • Proceedings of JMLR. Top ranking entrants will be invited to write a paper. • Best paper award: free WCCI registration. • Prizes: P(i)=$100. P = n*sum P(i). • Your problem solved by dozens of research groups: • help us organize the next challenge! clopinet.com/causality