• Area Covid-19
  • deplazio
    il Dipartimento di Epidemiologia del Servizio Sanitario del Lazio (DEP)
    con sede a Roma è un’istituzione attiva da oltre 30 anni
  • ambientale-valutativo
    il personale del DEP ha specifiche competenze di metodi
    epidemiologici in campo ambientale e valutativo
  • rischi ambientali
    Il DEP fornisce ai decisori le migliori conoscenze epidemiologiche
    per pianificare interventi di riduzione dei rischi per la salute
  • esiti
    Il DEP effettua studi di valutazione degli esiti delle cure sanitarie
  • cure sanitarie
    Il DEP fornisce evidenze per migliorare la qualità
    e l'efficacia delle cure sanitarie
  • inquinamento
    Il DEP valuta i rischi a breve e lungo termine associati
    all’esposizione ad inquinanti atmosferici
  • cambiamenti climatici
    Il DEP valuta l’impatto sulla salute dei cambiamenti climatici
    e degli eventi estremi ad essi associati

Registro Regionale Dialisi e Trapianti Lazio (RRDTL)

Rome, 13-17 July 2015 - Causal Inference in Epidemiology and applications to Environmental Health PDF Stampa E-mail

causal-inferenceA central issue in epidemiology is the evaluation of the causal nature of reported associations between exposure to defined risk factors or treatments and the occurrence of disease.


This issue is even more important in environmental health sciences, where most of the research is observational in nature and the ability of the investigator to control exposure assignment is limited or non-existent. Nonetheless, besides contributing to the understanding of disease causation, etiologic studies are commonly regarded as providing the scientific basis for the adoption of preventive actions. Therefore, it becomes necessary a clear definition of what is meant by “causal relationship”, how to properly design an epidemiological study to detect causal effects, and under which conditions and assumptions such an approach is feasible.

The course will introduce the concept of causal inference within the framework of experimental designs. Then, observational studies will be introduced and special emphasis will be devoted on the assumptions needed to estimate causal relationships. Different approaches will be presented, such as propensity score, inverse probability weighting and irregular assignment mechanisms. Mediation analysis will be introduced, and operative methods to decompose causal effects into direct, indirect, mediated and interactive effects will be presented.

Lectures will be held by researchers of the Universities of Turin, Florence and the Harvard School of Public Health. All lectures will be followed by afternoon sessions, led by Prof. Joel Schwartz, where case-studies will be presented and discussed. Finally, individual case-studies from students will be selected for discussion on the last day.

Classes are aimed to graduate students with limited experience in the causal inference framework and basic knowledge of epidemiology and statistics. Epidemiologists, biologists, statisticians, mathematicians and public health scientists and operators are eligible to attend.


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