Estimating causal effects among different events is of great importance to
critical fields such as drug development. Nevertheless, the data features
associated with events may be distributed across various silos and remain
private within respective parties, impeding direct information exchange between
them. This, in turn, can result in biased estimations of local causal effects,
which rely on the characteristics of only a subset of the covariates. To tackle
this challenge, we introduce an innovative disentangle architecture designed to
facilitate the seamless cross-silo transmission of model parameters, enriched
with causal mechanisms, through a combination of shared and private branches.
Besides, we introduce global constraints into the equation to effectively
mitigate bias within the various missing domains, thereby elevating the
accuracy of our causal effect estimation. Extensive experiments conducted on
new semi-synthetic datasets show that our method outperforms state-of-the-art
baselines.

通过介绍一种创新的解耦架构，该论文旨在解决各方之间因数据特征分布导致的信息交流障碍，以提高因果效应估计的准确性。该方法通过组合共享和私有分支并引入全局约束来促进模型参数的无缝跨领域传输，并有效减轻各个缺失域中的偏差，实验表明其优于最先进的基线模型。