  It is often critical for prediction models to be robust to distributional
shifts between training and testing data. From a causal perspective, the
challenge is to distinguish the stable causal relationships from the unstable
spurious correlations across shifts. 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. Theoretically,
we justify the robustness of the approach against feature shifts with the
knowledge from causal learning. Empirically, we evaluate the CTRF using both
synthetic data experiments and real-world experiments in the Bing Ads platform,
including a click prediction task and in the context of an end-to-end
counterfactual optimization system. The proposed CTRF produces robust
predictions and outperforms most baseline methods compared in the presence of
feature shifts.
