  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.
