  Predicting which action (treatment) will lead to a better outcome is a
central task in decision support systems. To build a prediction model in real
situations, learning from biased observational data is a critical issue due to
the lack of randomized controlled trial (RCT) data. To handle such biased
observational data, recent efforts in causal inference and counterfactual
machine learning have focused on debiased estimation of the potential outcomes
on a binary action space and the difference between them, namely, the
individual treatment effect. When it comes to a large action space (e.g.,
selecting an appropriate combination of medicines for a patient), however, the
regression accuracy of the potential outcomes is no longer sufficient in
practical terms to achieve a good decision-making performance. This is because
the mean accuracy on the large action space does not guarantee the nonexistence
of a single potential outcome misestimation that might mislead the whole
decision. Our proposed loss minimizes a classification error of whether or not
the action is relatively good for the individual target among all feasible
actions, which further improves the decision-making performance, as we prove.
We also propose a network architecture and a regularizer that extracts a
debiased representation not only from the individual feature but also from the
biased action for better generalization in large action spaces. Extensive
experiments on synthetic and semi-synthetic datasets demonstrate the
superiority of our method for large combinatorial action spaces.
