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.

提出了一种 causal transfer random forest 模型，它将原有的训练数据与来自一个随机实验的少量数据组合，使其对 feature shift 具有鲁棒性，并在点击预测等任务中表现出优越性。