  The development of causal prediction models is challenged by the fact that
the outcome is only observable for the applied (factual) intervention and not
for its alternatives (the so-called counterfactuals); in medicine we only know
patients' survival for the administered drug and not for other therapeutic
options. Machine learning approaches for counterfactual reasoning have to deal
with both unobserved outcomes and distributional differences due to non-random
treatment administration. Unsupervised domain adaptation (UDA) addresses
similar issues; one has to deal with unobserved outcomes -- the labels of the
target domain -- and distributional differences between source and target
domain. We propose Adversarial Distribution Balancing for Counterfactual
Reasoning (ADBCR), which directly uses potential outcome estimates of the
counterfactuals to remove spurious causal relations. We show that ADBCR
outcompetes state-of-the-art methods on three benchmark datasets, and
demonstrate that ADBCR's performance can be further improved if unlabeled
validation data are included in the training procedure to better adapt the
model to the validation domain.
