About Susan Gruber
Susan Gruber is founder and Principal of Putnam Data Sciences, LLC, a statistical consulting firm specializing in casual inference and machine learning approaches to risk score prediction. Her substantive focus is on initiating and facilitating methods development for detecting safety signals in electronic health data and applications of super learning in predictive modeling and propensity score estimation. Her key contributions in targeted learning include data-adaptive methods for propensity score estimation and the development of software for point treatment and longitudinal analyses. She holds a Ph.D. in Biostatistics, an MPH in Epidemiology and Biostatistics, and an MS in Computer Science.
|October 2017 - Present||Founder and Principal, Putnam Data Sciences, LLC|
|January 2016 - April 2017||Assistant Professor and Director, Biostatistics Center, Harvard Pilgrim Health Care Institute and Harvard Medical School ‐ Population Medicine|
|March 2014 - October 2015||Senior Director, Reagan-Udall Foundation for the FDA ‐ IMEDS-Methods Research|
Honors and Awards
- Eric Lehmann Award for Outstanding Ph.D. Dissertation in Theoretical Statistics
|June 2007 - May 2011||Ph.D., University of California, Berkeley, California ‐ Biostatistics|
|September 2005 - June 2007||MPH, University of California, Berkeley, California ‐ Epidemiology and Biostatistics|
|September 1987 - June 1989||MS, University of California, San Diego|
|September 1978 - June 1982||BA, Northwestern University|
Technical Reports (9)
Diagnosing and Responding to Violations in the Positivity Assumption U.C. Berkeley Division of Biostatistics Working Paper Series (2010)
The assumption of positivity or experimental treatment assignment requires that observed treatment levels vary within confounder strata. This article discusses the positivity assumption in the context of assessing model and parameter-speciﬁc identiﬁability of causal eﬀects. ...
Readings in Targeted Maximum Likelihood Estimation U.C. Berkeley Division of Biostatistics Working Paper Series (2009)
This is a compilation of current and past work on targeted maximum likelihood estimation. It features the original targeted maximum likelihood learning paper as well as chapters on super (machine) learning using cross validation, randomized ...
Targeted Maximum Likelihood Estimation: A Gentle Introduction U.C. Berkeley Division of Biostatistics Working Paper Series (2009)
This paper provides a concise introduction to targeted maximum likelihood estimation (TMLE) of causal effect parameters. The interested analyst should gain sufficient understanding of TMLE from this introductory tutorial to be able to apply the ...
Collaborative Targeted Maximum Likelihood Estimation U.C. Berkeley Division of Biostatistics Working Paper Series (2009)
Collaborative double robust targeted maximum likelihood estimators represent a fundamental further advance over standard targeted maximum likelihood estimators of causal inference and variable importance parameters. The targeted maximum likelihood approach involves fluctuating an initial density ...
Active presecription drug safety surveillance: Exploring OMOP 2011-2012 experiments OMOP community meeting (2013)
The Observational Medical Outcomes Partnership (OMOP), a consortium of pharmaceutical, FDA, and academic researchers focuses on developing and evaluating electronic records-based methods for enhancing post-market drug safety surveillance. The OMOP 2011-2012 experiment consists of applying ...
A Targeted Confounder Selection Strategy for Propensity Score Estimation McGill University (2013)
These slides provide an introduction to data-adaptive propensity score estimation, and the collaborative targeted maximum likelihood estimator (C-TMLE) of van der Laan and Gruber. The notation has been greatly simplified, which makes the work accessible ...