  In this paper, we outline a principled approach to estimate an individualized
treatment rule that is appropriate for data from observational studies where,
in addition to treatment assignment not being independent of individual
characteristics, some characteristics may affect treatment assignment in the
current study but not be available in future clinical settings where the
estimated rule would be applied. The estimation framework is quite flexible and
accommodates any prediction method that uses observation weights, where the
observation weights themselves are a ratio of two flexibly estimated propensity
scores. We also discuss how to obtain a trustworthy estimate of the rule's
population benefit based on simple propensity-score-based estimators of average
treatment effect. We implement our approach in the R package DevTreatRules and
share the code needed to reproduce our results on GitHub.
