The program will be organized around the main theme of causal inference in health policy. Inferring cause and effect relationships between disease exposures and health outcomes is of central importance in many real-world health policy problems involving big datasets, such as adverse medication effects, environmental exposures and cancer incidence, and long-term health outcomes in chronic disease populations. Randomized experiments are expensive, time-consuming, prone to subject selection biases, and often unethical. Hence, it is desirable to infer causal relationships using observational data arising from electronic databases. Causal inference topics that will be addressed through presentations, panel discussions, and small-group sessions include: graphical techniques for causal modeling; analytic techniques, including matching, propensity score and instrumental variable models; latent variable models; causal inference in longitudinal data.

Secondary themes that will be addressed throughout the workshop include methods for linking large databases, data extraction techniques for clinical, genetics, and diagnostic imaging data, and data quality evaluation. These secondary themes have been selected because they often have a large impact on the ability to test causal hypotheses in large health databases.

With support from: