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We propose a new causal inference framework to learn causal effects frommultiple, decentralized data sources in a federated setting. We introduce anadaptive transfer algorithm that learns the similarities among the data sourcesby utilizing Random Fourier Features to disentangle the loss function intomultiple components, each of which is associated with a data source. The datasources may have different distributions; the causal effects are independentlyand systematically incorporated. The proposed method estimates the similaritiesamong the sources through transfer coefficients, and hence requiring no priorinformation about the similarity measures. The heterogeneous causal effects canbe estimated with no sharing of the raw training data among the sources, thusminimizing the risk of privacy leak. We also provide minimax lower bounds toassess the quality of the parameters learned from the disparate sources. Theproposed method is empirically shown to outperform the baselines ondecentralized data sources with dissimilar distributions.

An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects