  Estimation of conditional average treatment effects (CATEs) is an important
topic in sciences. CATEs can be estimated with high accuracy if distributed
data across multiple parties can be centralized. However, it is difficult to
aggregate such data owing to privacy concerns. To address this issue, we
proposed data collaboration double machine learning, a method that can estimate
CATE models with privacy preservation of distributed data, and evaluated the
method through simulations. Our contributions are summarized in the following
three points. First, our method enables estimation and testing of
semi-parametric CATE models without iterative communication on distributed
data. Semi-parametric CATE models enable estimation and testing that is more
robust to model mis-specification than parametric models. Second, our method
enables collaborative estimation between multiple time points and different
parties. Third, our method performed equally or better than other methods in
simulations using synthetic, semi-synthetic and real-world datasets.
