Disentangle Estimation of Causal Effects from Cross-Silo Data

Autor:	Liu, Yuxuan, Wang, Haozhao, Wang, Shuang, He, Zhiming, Xu, Wenchao, Zhu, Jialiang, Yang, Fan
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Cryptography and Security Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by ICASSP 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.02154 Zobrazit plný text záznamu View this record from Arxiv