## Abstract

A Causal Bayesian network (CBN) is a well-known technique to represent causal

relations between variables. To learn the causal relations, experiments are needed in

general because from observational data alone we can only learn up to Markov

equivalence. The elicitation of this causal information from data provides useful insight in

the system under study. Most existing algorithms for causal discovery implicitly assume a

setting where all variables of the system under study are known and sufficient amount of

data is present to perform reliable statistical tests. These assumptions provide an ideal

framework, which facilitates the discovery task significantly. In this article we use existing

techniques from this ideal setting as a starting point for new algorithms in non-ideal

settings. We present two algorithms, UnCaDo and MyCaDo++, which can be used when

insufficient data is present to perform statistical tests reliably and when there are latent

variables respectively. Both techniques provide experimentation plans to recover the

causal structure.

relations between variables. To learn the causal relations, experiments are needed in

general because from observational data alone we can only learn up to Markov

equivalence. The elicitation of this causal information from data provides useful insight in

the system under study. Most existing algorithms for causal discovery implicitly assume a

setting where all variables of the system under study are known and sufficient amount of

data is present to perform reliable statistical tests. These assumptions provide an ideal

framework, which facilitates the discovery task significantly. In this article we use existing

techniques from this ideal setting as a starting point for new algorithms in non-ideal

settings. We present two algorithms, UnCaDo and MyCaDo++, which can be used when

insufficient data is present to perform statistical tests reliably and when there are latent

variables respectively. Both techniques provide experimentation plans to recover the

causal structure.

Original language | English |
---|---|

Number of pages | 35 |

Journal | I3 Journal |

Volume | 9 |

Publication status | Published - 2009 |

### Bibliographical note

Mohand Boughanem, Salem Benferhat, Guy Melançon## Keywords

- Causality
- Bayesian Networks
- Probabilistic Graphical Models