Please join for the next installment of the PDHP workshop series: Causal Inference In Observational Studies, presented by Michael Elliott of the University of Michigan Biostatistics & The Program In Survey & Data Science.  This workshop will cover techniques for causal inference using observational data, including theoretical concepts (causal inference via counterfactuals, and how causal inference can be produced from randomized designs), and applied techniques such as estimating causal effects via propensity modelling and matching techniques.  Examples and code using R will also be provided throughout the workshops.

Topics include:

• Causal inference via counterfactuals
• Why randomized designs yield causal inference
• Estimating causal effects via propensity modelling & matching
• Overlap and its impact on inference
• Hands-on practice with example and code using R