[BioC] MolPAGE Training Workshop on Causal Inference - 19-21 May 2008

Brian Tom brian.tom at mrc-bsu.cam.ac.uk
Tue Feb 19 12:17:19 CET 2008


I have been asked by Professor Luisa Bernardinelli to advertise the 
below workshop on Causal Inference, which takes place on the 19th to 
21st May 2008 in Pavia, Italy.


Brian Tom


MolPAGE - Molecular Phenotyping to Accelerate Genomic Epidemiology
Training WorkPackage


Pavia (I), May 19-21, 2008


Luisa Bernardinelli
Carlo Berzuini

Stijn Vansteelandt
Vanessa Didelez
Arvid Sjolander


Recent developments on causal inference within the statistical and
artificial intelligence literature have led to important new insights on 
to address problems of confounding and selection bias in a wide variety of
problem settings. The aim of this Course is to review these developments 
to provide state-of-the-art statistical solutions for genetic path
identification, and for dealing with problems of confounding due to
population admixture and of selection bias resulting from ascertainment
conditions in genetic association studies. The first day of the Course will
focus on probabilistic graphical models, which have their origins in 
path analysis and which provide a natural general framework for expressing
and manipulating many important concepts in statistical genetics. Local
computational algorithms can be described in this way, but also complex
issues of identification in forensic settings, for example, together with
genetic mapping and pedigree uncertainty can be handled in this context, as
can issues of causal inference and identification of regulatory networks.
This part of the course will introduce the basic ideas and illustrate how
graphical models can be used in a variety of settings. The second day of 
Course will focus on statistical techniques to adjust for measured
confounding. Specifically, we will discuss limitations of ordinary
regression adjustment and focus on successful alternatives, such as inverse
probability weighting estimators in marginal structural models and
G-estimators. These methods will be applied to detect gene-environment
interactions in family-based association studies and to identify genetic
pathways. The third day of the Course will focus on the use of Mendelian
randomization for examining the causal effect of a modifiable exposure on
disease by making use of measured variation in genes of known function.
Emphasis will be on the assumptions required for using these methods and on
state-of-the-art analysis techniques. We will end with an introduction to
graphical search algorithms for unravelling causal genetic networks. The
Course will include hands-on computer practical sessions during the
afternoons. It is intended for an audience with some previous familiarity
with statistics, in particular with linear and logistic regression.

For further information, please do not hesitate to contact:
Alessandra Bianchi
Assistant to Prof. Luisa Bernardinelli Università di Pavia
Dip.to Scienze Sanitarie Applicate e Psicocomportamentali
Sezione di Statistica medica ed Epidemiologia via A. Bassi, 21 27100 Pavia
tel 0382 987 541 fax 0382 987 570
e-mail lab.statgen at unipv.it

Brian D M Tom (PhD)
MRC Biostatistics Unit
Institute of Public Health
University Forvie Site
Robinson Way
Cambridge CB2 0SR
United Kingdom

Tel  +44 1223 330 382
Fax  +44 1223 330 388

e-mail brian.tom at mrc-bsu.cam.ac.uk

More information about the Bioconductor mailing list