Estimation of conditional average treatment effects on distributed data:
  A privacy-preserving approach
Yuji Kawamata Ryoki Motai Yukihiko Okada Akira Imakura Tetsuya Sakurai
Estimation of conditional average treatment effects (CATEs) is an important
topic in various fields such as medical and social 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 if they contain
privacy information. To address this issue, we proposed data collaboration
double machine learning (DC-DML), a method that can estimate CATE models with
privacy preservation of distributed data, and evaluated the method through
numerical experiments. 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 or non-parametric CATE models enable estimation and testing
that is more robust to model mis-specification than parametric models. However,
to our knowledge, no communication-efficient method has been proposed for
estimating and testing semi-parametric or non-parametric CATE models on
distributed data. Second, our method enables collaborative estimation between
different parties as well as multiple time points because the
dimensionality-reduced intermediate representations can be accumulated. Third,
our method performed as well or better than other methods in evaluation
experiments using synthetic, semi-synthetic and real-world datasets.