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Causality Workbench clopinet.com/causality
Cause-Effect Pair Challenge
Isabelle Guyon, ChaLearn
IJCNN 2013IEEE/INNS
Causality Workbench clopinet.com/causality
Causal discovery
Which actions will have beneficial effects?
…your health?
…climate changes?… the economy?
What affects…
Causality Workbench clopinet.com/causality
Available data
• A lot of “observational” data.
Correlation Causality!
• Experiments are often needed, but:– Costly– Unethical– Infeasible
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 Workbench clopinet.com/causality
Causality Workbench clopinet.com/causality
Lung Cancer
Smoking Genetics
Coughing
AttentionDisorder
Allergy
Anxiety Peer Pressure
Yellow Fingers
Car Accident
Born an Even Day
Fatigue
Causal graph example
Causality Workbench clopinet.com/causality
Lung Cancer
Smoking Genetics
Coughing
AttentionDisorder
Allergy
Anxiety Peer Pressure
Yellow Fingers
Car Accident
Born an Even Day
Fatigue
Causality assessmentwith experiments
Causality Workbench clopinet.com/causality
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 BBut…•Such methods require a lot of data to work well and often rely on simplifying assumptions (e.g. “causal sufficiency”, “faithfulness”, linearity, Gaussian noise)
Cause-effect pair problem
Causality Workbench clopinet.com/causality
Lung CancerSmoking
Genetics
Fatigue Lung Cancer
Lung CancerAttentionDisorder
Born an Even Day
Lung Cancer
A B
A -> B
A <- B
A – B
A | B
Typical method
Causality Workbench clopinet.com/causality
Test whether A -> B is a better explanation than A <- B comparing two models:
B = f (A, noise) A = f (B, noise)
Scoring
Causality Workbench clopinet.com/causality
S0
A -> B A <- BA – 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 vs. A – B, A | B, A -> B
A ? B
Causality Workbench clopinet.com/causality
A
BA ->
BB =Altitude
A = Temperature
Causality Workbench clopinet.com/causality
A
BA <-
B B =Wages
A = Age
A ? B
Causality Workbench clopinet.com/causality
A
B A | B
A ? B
Causality Workbench clopinet.com/causality
A
B A - B
A ? B
Causality Workbench clopinet.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!
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 Workbench clopinet.com/causality
