Causal Interpretation and Identification
of Conditional Independence Structures

## Seminar 3 on LEARNING CAUSAL MODELS

November 2 - 12, 1999

### Organizing Committee:

David Heckerman, *Microsoft Research*

Steffen Lauritzen, *Aalborg University*

### Overview:

Seminar 1 and 2 introduce connections between causal interpretations
of graphs and their conditional independence properties. This seminar
will discuss how these connections can be applied to the problem of
learning about causal relations from data.

We consider both Bayesian and asymptotic approaches, with an emphasis
on the former. We relate causal interpretations to commonly used assumptions
used for the selection of graph structure such as parameter independence,
parameter modularity, and marginal likelihood equivalence. In addition,
we address difficulties in scoring and searching over graphical models
with latent variables, compare model selection to model averaging techniques,
and discuss assumptions under which "counterfactual" information can
be learned.

### INVITED SPEAKERS

**G. Cooper** *University of Pittsburgh* |
**J. Andersen** *Aalborg University* |

**B. Frey** *University of Waterloo* |
**J. Cheng** *University of Alberta* |

**T. Richardson** *University of Warwick* |
**G. Shafer** *Rutgers University* |

**P. Giudici** *University of Pavia* |
**J. Whittaker** *Lancaster University* |

**R. Shachter** *Stanford University* |