Causality Workbenchclopinet.com/causality Challenges in Causality Isabelle Guyon, Clopinet Constantin Aliferis and Alexander Statnikov, Vanderbilt Univ

Causality Workbench clopinet.com/causality

Challenges in Causality

Isabelle Guyon, ClopinetConstantin 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)


An engineering view…


The system

Systemic causality

External agent


Feature Selection

X

Y

Predict Y from features X1, X2, …

Select most predictive features.


X

Y

Causation

Predict the consequences of actions:

Under “manipulations” by an external agent, some features are no longer predictive.

Y


What is out there?


Available data

• A lot of “observational” data.

Correlation Causality!

• Experiments are often needed, but:– Costly– Unethical– Infeasible


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.


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


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?


First datasets

Toy datasets

Challenge datasets


On-line feed-back


Our challenges

Find…

• Problems

• Data

• Metrics

• Challenge protocols

• Implementation


Ecology

0 2000 4000 6000 8000 10000 12000 14000 160000

10

20

30

40

50

60

70

80

90

100

DALTON

Healthcaremass spec

Upcoming datasets

ECONO

Marketing

TIED

Conceptual

Psychology

Epidemiology

InternetClimatology

Neuroscience

Security Sociology


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


Performance assessment


Metrics

• Fulfillment of an objective:• Future (prediction)

• Past (counterfactual)

• Causal relationships:• Existence

• Strength

• Degree


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


Lung Cancer

Smoking Genetics

Coughing

AttentionDisorder

Allergy

Anxiety Peer Pressure

Yellow Fingers

Car Accident

Born an Even Day

Fatigue

LUCAS0: natural

Causality assessmentwith manipulations


LUCAS1: manipulate

d

Lung Cancer

Smoking Genetics

Coughing

AttentionDisorder

Allergy


Yellow Fingers

Car Accident

Born an Even Day

Fatigue



Lung Cancer

Smoking Genetics

Coughing

AttentionDisorder

Allergy


Yellow Fingers

Car Accident

Born an Even Day

Fatigue

LUCAS2: manipulate

d



Goal driven causality

0

9 4

11

61

10 2

3

7

5

8

• We define: V=variables of interest

(e.g. MB, direct causes, ...)

• We assess causal relevance: R=f(V,S).

4 11 2 3 1

• Participants return: S=selected subset

(ordered or not).


Causality assessmentwithout manipulation?


Using artificial “probes”

Lung Cancer

Smoking Genetics

Coughing

AttentionDisorder

Allergy


Yellow Fingers

Car Accident

Born an Even Day

FatigueLUCAP0: natural

Probes

P1 P2 P3 PT


Probes

Lung Cancer

Smoking Genetics

Coughing

AttentionDisorder

Allergy


Yellow Fingers

Car Accident

Born an Even Day

Fatigue

P1 P2 P3 PT

LUCAP1&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 = (NTruePos/NReal) Rscore + 0.5 (NTrueNeg/NReal)


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!

Documents

Causality Workbenchclopinet.com/causality Challenges in Causality Isabelle Guyon, Clopinet Constantin Aliferis and Alexander Statnikov, Vanderbilt Univ