Collaborative Double Robustness and the Connection to Data-Adaptive Nuisance Model Selection in Causal Inference

Mireille Schnitzer - Assistant Professor of Biostatistics, University of Montreal

Thursday, March 10, 2016
Time: 10:30 a.m. – 12:00 p.m. Pacific / 1:30 p.m. – 3:00 p.m. Eastern
Host Location: Santa Monica (room m5312)
Other Locations: Pittsburgh (room p4318), Washington, D.C.(room w4128)


In causal inference and censored data methods, double robust estimators such as Targeted Minimum Loss-based estimation require the specification of two model components. Estimation will be consistent under the correct specification of either nuisance component, conditional on a sufficient set of confounding variables. However, this class of estimators also has a collaborative robustness property that allows for consistent effect estimation in a wider class of settings. This property can be useful for guiding both manual and automated variable selection. We describe this class of estimators in single time point and longitudinal exposure settings, describe a cohesive approach to variable selection in causal inference, and illustrate the performance of data-adaptive learning algorithms for the nuisance models.

About the Presenter

Mireille Schnitzer is an Assistant Professor of Biostatistics at the University of Montreal. Mireille received her PhD in Biostatistics from McGill University in 2012 and was a postdoctoral research fellow at the Harvard T.H. Chan School of Public Health in 2013. She also had the opportunity to study causal inference at U.C. Berkeley and the University of Pennsylvania as a visiting student. Mireille's current research interests are causal inference methodology in the fields of pharmacoepidemiology and population surveillance, the usage of electronic medical databases, semiparametric efficient estimation in longitudinal and survival settings, and meta-analysis. Mireille currently holds NSERC Discovery and Accelerator grants, a CIHR New Investigator Salary Award and is also funded by the Fonds de Recherche du Québec, Santé and by the Faculté de pharmacie at Université de Montréal.

To Attend

Visitors to RAND's Santa Monica and Pittsburgh locations are welcome to attend & must RSVP at least one day prior to the seminar. To ensure attendance please, contact Emily Payne with your name, company or affiliation & national citizenship (for security purposes).

Sponsored by the RAND Statistics Group.