Challenges in causality: Results of the WCCI 2008 challenge 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

Causal discovery Which actions will have beneficial effects? …your health? …climate changes? … the economy? What affects…

What is causality? Many definitions: –Science –Philosophy –Law –Psychology –History –Religion –Engineering “Cause is the effect concealed, effect is the cause revealed” (Hindu philosophy)

The system Systemic causality External agent

Difficulty A lot of “observational” data. Correlation  Causality! Experiments are often needed, but: –Costly –Unethical –Infeasible

Causality workbench

Our approach What is the causal question? Why should we care? What is hard about it? Is this solvable? Is this a good benchmark?

Four tasks Toy datasets Challenge datasets

On-line feed-back

Toy Examples

Lung Cancer SmokingGenetics Coughing Attention Disorder Allergy AnxietyPeer Pressure Yellow Fingers Car Accident Born an Even Day Fatigue LUCAS 0 : natural Causality assessment with manipulations

LUCAS 1 : manipulated Lung Cancer Smoking Genetics Coughing Attention Disorder Allergy AnxietyPeer Pressure Yellow Fingers Car Accident Born an Even Day Fatigue Causality assessment with manipulations

Lung Cancer SmokingGenetics Coughing Attention Disorder Allergy AnxietyPeer Pressure Yellow Fingers Car Accident Born an Even Day Fatigue LUCAS 2 : manipulated Causality assessment with manipulations

Goal driven causality We define: V=variables of interest (e.g. MB, direct causes,...) We assess causal relevance: Fscore=f(V,S) Participants return: S=selected subset (ordered or not).

Causality assessment without manipulation?

Using artificial “probes” Lung Cancer SmokingGenetics Coughing Attention Disorder Allergy AnxietyPeer Pressure Yellow Fingers Car Accident Born an Even Day Fatigue LUCAP 0 : natural Probes P1P1 P2P2 P3P3 PTPT

Lung Cancer SmokingGenetics Coughing Attention Disorder Allergy AnxietyPeer Pressure Yellow Fingers Car Accident Born an Even Day Fatigue LUCAP 0 : natural Probes P1P1 P2P2 P3P3 PTPT Using artificial “probes”

Probes Lung Cancer SmokingGenetics Coughing Attention Disorder Allergy AnxietyPeer Pressure Yellow Fingers Car Accident Born an Even Day Fatigue P1P1 P2P2 P3P3 PTPT LUCAP 1&2 : manipulated Using artificial “probes”

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 = (N TruePos /N Real ) Rscore (N TrueNeg /N Real )

Results

AUC distribution

Methods employed Causal: Methods employing causal discovery technique to unravel cause-effect relationships in the neighborhood of the target. Markov blanket: Methods for extracting the Markov blanket, without attempting to unravel cause-effect relationships. Feature selection: Methods for selecting predictive features making no explicit attempt to uncover the Markov blanket or perform causal discovery.

Formalism: Causal Bayesian networks Bayesian network: –Graph with random variables X 1, X 2, …X n as nodes. –Dependencies represented by edges. –Allow us to compute P(X 1, X 2, …X n ) as  i P( X i | Parents(X i ) ). –Edge directions have no meaning. Causal Bayesian network: egde directions indicate causality.

Causal discovery from “observational data” Example algorithm: PC (Peter Spirtes and Clarck Glymour, 1999) Let A, B, C  X and V  X. Initialize with a fully connected un-oriented graph. 1. Conditional independence. Cut connection if  V s.t. (A  B | V). 2. Colliders. In triplets A — C — B (A — B) if there is no subset V containing C s.t. A  B | V, orient edges as: A  C  B. 3. 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.

Computational and statistical complexity Computing the full causal graph poses: Computational challenges (intractable for large numbers of variables) Statistical challenges (difficulty of estimation of conditional probabilities for many var. w. few samples). Compromise: Develop algorithms with good average- case performance, tractable for many real-life datasets. Abandon learning the full causal graph and instead develop methods that learn a local neighborhood. Abandon learning the fully oriented causal graph and instead develop methods that learn unoriented graphs.

Target Y A prototypical MB algo: HITON Aliferis-Tsamardinos-Statnikov, 2003

Target Y 1 – Identify variables with direct edges to the target (parent/children) Aliferis-Tsamardinos-Statnikov, 2003

Target Y Aliferis-Tsamardinos-Statnikov, – Identify variables with direct edges to the target (parent/children) A B Iteration 1: add A Iteration 2: add B Iteration 3: remove A because A  Y | B etc. A A B B

Target Y Aliferis-Tsamardinos-Statnikov, – Repeat algorithm for parents and children of Y (get depth two relatives)

Target Y Aliferis-Tsamardinos-Statnikov, – Remove non-members of the MB A member A of PCPC that is not in PC is a member of the Markov Blanket if there is some member of PC B, such that A becomes conditionally dependent with Y conditioned on any subset of the remaining variables and B. A B

Collider Spouse Target Y Spouse Collider Aliferis-Tsamardinos-Statnikov, – Orient edges 1. Colliders: The presence of a spouse determines a collider. The target may also be a collider (B  C | Y). B C

Collider Spouse Target Y Spouse Collider Aliferis-Tsamardinos-Statnikov, – Orient edges 1. Colliders: The presence of a spouse determines a collider. The target may also be a collider (B  C | Y). 2.Orient remaining edges. B C

Additional Bells and Whistles The basic algorithms make simplifying assumptions: – Faithfulness (any conditional independence between two variables results in an absence of direct edge.) – Causal sufficiency (there are no unobserved common causes of the observed variables.) Laura E. Brown &Ioannis Tsamardinos: – Violations of “ faithfulness ” : select product of features. – Violation of “ causal sufficiency ” : use Y structures.

Discussion

Top ranking methods According to the rules of the challenge: –Yin Wen Chang: SVM => best prediction accuracy on REGED and CINA. –Gavin Cawley: Causal explorer + linear ridge regression ensembles => best prediction accuracy on SIDO and MARTI. According to pairwise comparisons: –Jianxin Yin and Prof. Zhi Geng’s group: Partial Orientation and Local Structural Learning => best on Pareto front, new original causal discovery algorithm.

Pairwise comparisons

Causal vs. non-causal Jianxin Yin: causal Vladimir Nikulin: non-causal

Using manip-MB as feature set  using a causal model Unmanipulated (training) Manipulation #1 (test) Heuristic: (1) Use the post-manipulation MB as feature set; (2) train a classifier to predict Y on training data (from the unmanipulated distribution). Manipulation #2 (test) Problem: Manipulated children of the target may remain in the post-manipulation MB (if they are also spouses) but with a different dependency to the target.

MB is not the best feature set? Some features outside the MB may enhance predictivity if: a. Some MB features go undetected (e.g. the direct causes are children of a common ancestor). b. The predictor is too “weak” (e.g. the relationship to the target is non-linear but the predictor is linear). Y X Z y=a x 2 z= x 2 (a) (b)

Insensitivity to irrelevant features Simple univariate predictive model, binary target and features, all relevant features correlate perfectly with the target, all irrelevant features randomly drawn. With 98% confidence, abs(feat_weight) < w and  i w i x i < v. n g number of “ good ” (relevant) features n b number of “ bad ” (irrelevant) features m number of training examples.

Conclusion Causal discovery from observational data is not an impossible task, but a very hard one. This points to the need for further research and benchmark. Don’t miss the “pot-luck challenge”

1) 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, ) Design and Analysis of the Causation and Prediction Challenge I. Guyon, C. Aliferis, G. Cooper, A. Elisseeff, J.-P. Pellet, P. Spirtes, A. Statnikov, JMLR workshop proceedings, in press.