2023/01/01 by Thanh Vinh Vo, Vo, Thanh Vinh, Arnab Bhattacharyya +5 · 2 citations
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Data Quality and Management #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.2301.00346
openalex publication_date 2023/01/01 · openalex created_date 2023/01/06 · openalex updated_date 2026/07/28
We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting. We introduce an adaptive transfer algorithm that learns the similarities among the data sources by utilizing Random Fourier Features to disentangle the loss function into multiple components, each of which is associated with a data source. The data sources may have different distributions; the causal effects are independently and systematically incorporated. The proposed method estimates the similarities among the sources through transfer coefficients, and hence requiring no prior information about the similarity measures. The heterogeneous causal effects can be estimated with no sharing of the raw training data among the sources, thus minimizing the risk of privacy leak. We also provide minimax lower bounds to assess the quality of the parameters learned from the disparate sources. The proposed method is empirically shown to outperform the baselines on decentralized data sources with dissimilar distributions.