  Off-policy evaluation (OPE) is the problem of estimating the value of a
target policy from samples obtained via different policies. Recently, applying
OPE methods for bandit problems has garnered attention. For the theoretical
guarantees of an estimator of the policy value, the OPE methods require various
conditions on the target policy and policy used for generating the samples.
However, existing studies did not carefully discuss the practical situation
where such conditions hold, and the gap between them remains. This paper aims
to show new results for bridging the gap. Based on the properties of the
evaluation policy, we categorize OPE situations. Then, among practical
applications, we mainly discuss the best policy selection. For the situation,
we propose a meta-algorithm based on existing OPE estimators. We investigate
the proposed concepts using synthetic and open real-world datasets in
experiments.
