CausalityCausalityStefan Segers, KU Leuven

Problem: Creating a Causal Bayessian Network

from an empirical Data Set

IC* Algorithm, determine relationship

from an empirical Data Set

Example:Method:

IC* Algorithm, determine relationship

between nodes:

1. Potential Cause

2. Genuine Cause

Agility

2. Genuine Cause

3. Spurious Association

4. Undefined

Tools :

Tennis

BNT: Toolbox for Matlab

Tools :

Concept:

Real Network

Concept:

The decline of the amount of pirates in the world

causes global warming?causes global warming?

Results/Conclusions: Real Data example:

• Causal Relationships can only be found if

V-structures are naturally present in the

Southerner

V-structures are naturally present in the

data – one data set can often define

multiple correct causal bayessian networks

Gender• Correctness of output dependant on

hidden statistical parameters, discretisation

of continuous variablesof continuous variables

• Determinism (functional relationships) in

data needs to be accounted for, or causal data needs to be accounted for, or causal

relationships will not be found

References: Judea Pearl, Kevin Murphy, Richard Scheines

CausalityCausalityStefan Segers, KU Leuven

Creating a Causal Bayessian Network

from an empirical Data Setfrom an empirical Data Set

Example:

StaminaAgility StaminaAgility

Marathon MarathonTennis

Wealth Wealth

Concept: Latent Variables

samplingReal Network Network, output IC*

Concept: Latent Variables

The decline of the amount of pirates in the world

auses global warming?auses global warming?

Incorrect, only a correlation between the topics

Causal Relationship is a latent (unobserved) Causal Relationship is a latent (unobserved)

variable that causes both (“Time”)

Real Data example: American Census

Southerner Race

Wage

Gender

Union

Occupation

Union

MemberSectorMarital

Status

(sample of 534 persons, IC* output)

References: Judea Pearl, Kevin Murphy, Richard Scheines