PUBHL Biostatistics Division
School of Public Health
Twin Cities
School of Public Health
Twin Cities
Project Title:
Methods for Causal Interference and Dynamic Treatment Regimes
This group develops novel computational tools to estimate the causal effect of an intervention or action. Particular interests include:
- Variable selection techniques for causal inference
- Methods when the intervention is reported with error (e.g., compliance)
- Power formula for developing a dynamic treatment regime
- Methods to improve SMART designs
Project Investigators
Charles Cain
Matt Caputo
Chuyu Deng
Grace Lyden
Alyssa Montgomery
Ross Peterson
Aparajita Sur
Dr. David Vock
Jack Wolf
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