Bayesian Federated Estimation of Causal Effects from Observational Data

被引:0
|
作者
Thanh Vinh Vo [1 ]
Lee, Young [2 ]
Trong Nghia Hoang [3 ]
Leong, Tze-Yun [1 ]
机构
[1] Natl Univ Singapore, Sch Comp, Singapore, Singapore
[2] Harvard Univ, Cambridge, MA 02138 USA
[3] Washington State Univ, Sch Elect Engn & Comp Sci, Pullman, WA 99164 USA
来源
UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, VOL 180 | 2022年 / 180卷
基金
新加坡国家研究基金会;
关键词
INFERENCE;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We propose a Bayesian framework for estimating causal effects from federated observational data sources. Bayesian causal inference is an important approach to learning the distribution of the causal estimands and understanding the uncertainty of causal effects. Our framework estimates the posterior distributions of the causal effects to compute the higher-order statistics that capture the uncertainty. We integrate local causal effects from different data sources without centralizing them. We then estimate the treatment effects from observational data using a non-parametric reformulation of the classical potential outcomes framework. We model the potential outcomes as a random function distributed by Gaussian processes, with defining parameters that can be efficiently learned from multiple data sources. Our method avoids exchanging raw data among the sources, thus contributing towards privacy-preserving causal learning. The promise of our approach is demonstrated through a set of simulated and real-world examples.
引用
收藏
页码:2024 / 2034
页数:11
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