Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction

no code implementations • 17 Oct 2020 • Shuxi Zeng, Murat Ali Bayir, Joesph J. Pfeiffer III, Denis Charles, Emre Kiciman

We describe a causal transfer random forest (CTRF) that combines existing training data with a small amount of data from a randomized experiment to train a model which is robust to the feature shifts and therefore transfers to a new targeting distribution.

counterfactual