  Decision-making often requires accurate estimation of treatment effects from
observational data. This is challenging as outcomes of alternative decisions
are not observed and have to be estimated. Previous methods estimate outcomes
based on unconfoundedness but neglect any constraints that unconfoundedness
imposes on the outcomes. In this paper, we propose a novel regularization
framework for estimating average treatment effects that exploits
unconfoundedness. To this end, we formalize unconfoundedness as an
orthogonality constraint, which ensures that the outcomes are orthogonal to the
treatment assignment. This orthogonality constraint is then included in the
loss function via a regularization. Based on our regularization framework, we
develop deep orthogonal networks for unconfounded treatments (DONUT), which
learn outcomes that are orthogonal to the treatment assignment. Using a variety
of benchmark datasets for estimating average treatment effects, we demonstrate
that DONUT outperforms the state-of-the-art substantially.
