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.
翻译:我们提出了一个新的因果推断框架,以在联盟环境下从多个分散的数据源中了解因果效应。我们引入了适应性转移算法,通过利用随机多功能将损失函数分解成多个组成部分,每个组成部分都与数据源相关。数据源可能有不同的分布;因果效应是独立和系统地结合的。拟议方法通过转移系数估算来源之间的相似性,因此不需要事先提供关于类似性计量的信息。如果来源之间不共享原始培训数据,就可以估算各种因果效应,从而最大限度地减少隐私泄漏的风险。我们还提供小尺寸较低的界限,以评估从不同来源获得的参数的质量。提议的方法在经验上显示,超出了分散数据源的基线,分布不相似。