At Putnam Data Sciences we combine machine learning with sound principles of causal inference to generate reliable answers to your most important questions.

Our areas of expertise include causal effect estimation of point treatment and longitudinal treatment regimes using TMLE, marginal structural modeling, IPTW, and other propensity score-based methodologies.

We also specialize in risk prediction modeling to aid in identifying high risk individuals. Model development utilizes advanced machine learning methodologies such as lasso, neural networks, SVMs, gradient boosting and super learning.

Recent News

Susan Gruber is chairing this year’s Atlantic Causal Inference Conference Data Challenge. Inaugurated in 2016, the Data Challenge provides a level playing field for blinded comparison of cutting edge causal inference methodologies. Details available on the ACIC Data Challenge website. Results will be presented at the conference, to be held May 22 – 24 at McGill University in Montréal, Canada. Details available on the ACIC, 2019 Conference website.


R Packages for targeted minimum loss based estimation (TMLE)

  • tmle: Analysis of point treatment data to estimate average treatment effect among the population (ATE), among the treated (ATT), and among the controls (ATC) using targeted minimum loss-based estimation of point treatment effects (tmle),  and estimation of the parameters of a marginal structural model (tmleMSM).
  • ltmle: Longitudinal data analysis using targeted minimum loss-based estimation.
  • ctmle: Collaborative targeted minimum loss-based estimation of point treatment effects using data adaptive propensity score estimation.

Selected Publications


Putnam Data Sciences, LLC was founded in 2017 by Susan Gruber, PhD, MPH, MS.  Dr. Gruber is a biostatistician and computer scientist whose expertise is in the development and application of data adaptive methodologies to improve the quality of evidence generated by studies of observational health care data.  Dr. Gruber was formerly the Director of the Biostatistics Center in the Department of Population Medicine at Harvard Medical School & Harvard Pilgrim Health Care Institute, and Senior Director of IMEDS Methods Research for the Reagan-Udall Foundation for the FDA.

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85 Putnam Avenue, Suite 2, Cambridge, MA, 02139

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