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.
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.