  Machine learning models are often trained on data from one distribution and
deployed on others. So it becomes important to design models that are robust to
distribution shifts. Most of the existing work focuses on optimizing for either
adversarial shifts or interventional shifts. Adversarial methods lack
expressivity in representing plausible shifts as they consider shifts to joint
distributions in the data. Interventional methods allow more expressivity but
provide robustness to unbounded shifts, resulting in overly conservative
models. In this work, we combine the complementary strengths of the two
approaches and propose a new formulation, RISe, for designing robust models
against a set of distribution shifts that are at the intersection of
adversarial and interventional shifts. We employ the distributionally robust
optimization framework to optimize the resulting objective in both supervised
and reinforcement learning settings. Extensive experimentation with synthetic
and real world datasets from healthcare demonstrate the efficacy of the
proposed approach.
