Disentangle Estimation of Causal Effects from Cross-Silo Data
Yuxuan Liu Haozhao Wang Shuang Wang Zhiming He Wenchao Xu Jialiang Zhu Fan Yang
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.