Appendix: AnAdaptiveKernelApproachtoFederatedLearning ofHeterogeneousCausalEffects

For example, if an individual appears in all of the sources, the trained model would be biased by data of this individual (there is imbalance caused by the use of more data from this particular individual than the others). Hence, this condition would ensure that such bias does not exist. Toaddress suchaproblem, wepropose a pre-training step to exclude such duplicated individuals. The pre-training step are summarized as follows: (1) Suppose thatanindividual canbeuniquely identified viaasetoffeatures. The causal effects are unidentifiable if the confounders are unobserved.