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.

翻译：在药物开发等关键领域，不同事件之间的因果效应估计具有重要意义。然而，与事件相关的数据特征可能分布在多个孤岛中，且在各自方内部保持私密，阻碍了它们之间的直接信息交换。这进而可能导致仅依赖部分协变量特征的局部因果效应估计产生偏差。为解决这一挑战，我们提出了一种创新的解缠架构，通过共享分支与私有分支的组合，促进携带因果机制的模型参数在跨孤岛间无缝传输。此外，我们在方程中引入全局约束，有效缓解不同缺失域内的偏差，从而提升因果效应估计的精度。在新型半合成数据集上进行的大量实验表明，我们的方法优于现有最先进的基线方法。