Disentangle Estimation of Causal Effects from Cross-Silo Data
This addresses a privacy-preserving causal inference problem for fields like drug development where data cannot be directly shared.
The paper tackles the problem of estimating causal effects when data is distributed across private silos, which can lead to biased local estimates. Their method uses a disentangled architecture with shared/private branches and global constraints to transmit model parameters, achieving superior performance over state-of-the-art baselines on semi-synthetic datasets.
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.