Federated Causal Inference from Observational Data
观察数据中的联邦因果推断AI 解读 ↓
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观察数据中的联邦因果推断
- 研究问题
- 当观察数据分散于不能共享原始记录的来源，且来源分布不同或存在缺失时，如何估计因果效应及其不确定性？
- 方法与思路
- 作者提出联邦框架，分别用高斯过程、随机傅里叶特征与自适应迁移处理效应估计、来源差异和随机缺失数据。
- 材料陈述的贡献
- 摘要介绍FedCI、CausalRFF和CausalFI三个实例，在不交换原始数据的条件下估计效应、来源相似性及效应高阶统计量。
- 阅读关注点
- 应核对因果识别和随机缺失假设、来源间迁移机制、交换信息的内容，以及效应后验与高阶统计量的可靠性。
- 阅读边界
- 这是arXiv预印本，同行评审及接收状态未核实；摘要未给出完整识别条件或形式化隐私保证，不共享原始数据不等于证明隐私安全。
原文摘要
Decentralized data sources are prevalent in real-world applications, posing a formidable challenge for causal inference. These sources cannot be consolidated into a single entity owing to privacy constraints. The presence of dissimilar data distributions and missing values within them can potentially introduce bias to the causal estimands. In this article, we propose a framework to estimate causal effects from decentralized data sources. The proposed framework avoid exchanging raw data among the sources, thus contributing towards privacy-preserving causal learning. Three instances of the proposed framework are introduced to estimate causal effects across a wide range of diverse scenarios within a federated setting. (1) FedCI: a Bayesian framework based on Gaussian processes for estimating causal effects from federated observational data sources. It estimates the posterior distributions of the causal effects to compute the higher-order statistics that capture the uncertainty. (2) CausalRFF: 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. It estimates the similarities among the sources through transfer coefficients, and hence requiring no prior information about the similarity measures. (3) CausalFI: a new approach for federated causal inference from incomplete data, enabling the estimation of causal effects from multiple decentralized and incomplete data sources. It accounts for the missing data under the missing at random assumption, while also estimating higher-order statistics of the causal estimands. The proposed federated framework and its instances are an important step towards a privacy-preserving causal learning model.