  In this work, we consider the problem of estimating a behaviour policy for
use in Off-Policy Policy Evaluation (OPE) when the true behaviour policy is
unknown. Via a series of empirical studies, we demonstrate how accurate OPE is
strongly dependent on the calibration of estimated behaviour policy models: how
precisely the behaviour policy is estimated from data. We show how powerful
parametric models such as neural networks can result in highly uncalibrated
behaviour policy models on a real-world medical dataset, and illustrate how a
simple, non-parametric, k-nearest neighbours model produces better calibrated
behaviour policy estimates and can be used to obtain superior importance
sampling-based OPE estimates.
