We are pleased to announce that Carlos Cinelli, Assistant Professor of Statistics, has received grant funding support from the Royalty Research Fund (RRF) for his proposal “Sensitivity Analysis for Machine Learned Instrumental Variable Models”.
Instrumental variable (IV) methods are widely used in the social and health sciences for drawing causal inferences from observational data. These methods, however, require strong, often untestable assumptions that are hard to defend in practice. In this proposal, Cinelli develops tools that allow researchers to quantify how strong these violations need to be in order to change the conclusions of an IV study. This proposal is part of Cinelli's broader research agenda that develops new theory, methods, and open-source software for permitting causal inferences under more flexible and realistic settings.
Congratulations to Carlos Cinelli on his success!
Cinelli Receives Royalty Research Fund (RRF) Award
