Before revision:  We introduce inclusion-probability weighting as a form of cross-splitting and averaging for each observation to avoid biases through sample splitting.
After revision:  We find that our proposed method has lower absolute errors as well as smaller bias than the benchmark doubly-robust estimator. We further introduce a bagging type averaging for the CATE function for each observation to avoid biases through sample splitting.