The goal of many observational studies is to quantify the causal effect of an exposure on an outcome. However, most statistical methods provide measures of association (which may lack a causal interpretation) and practical causal questions (such as whether an adjustment for a given confounder is appropriate) have resisted formal treatment. In this school, I will give an overview of the latest methodologic developments for estimating and identifying causal effects from observational data. Causal concepts such as causal effect, confounding, ... will be formally defined using counterfactual theory.
Directed acyclic graph theory will be used to control for confounding bias. Finally, marginal structural models will be introduced to estimate the causal effect of a time-varying exposure in the presence of time-dependent confounders that lie on the causal pathway between exposure and outcome. Examples from public health studies will be provided.

How to Apply for the BIOSTAT 2004
The ITI Conference participants who wish to join the Meeting or School should register using the ITI Registration Form (http://iti.srce.hr/itireg.html). Please indicate on the Form your intention to participate in the School of Biometrics, in order to receive the School handouts at the Conference. For the ITI participants, there is no additional fee for the School. Submitted papers or poster abstracts (http://iti.srce.hr/submit.html) should reach ITI Conference secretariat within ITI deadlines and should follow ITI Instructions to authors (http://iti.srce.hr/itinstr.html). Accepted papers will be presented within the ITI topic: Data Mining, Statistics and Biometrics.
The detailed program will be announced later, on the ITI web site at http://iti.srce.hr/.

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