Estimation of conditional average treatment effects on distributed data: A privacy-preserving approach

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.