  Local decision rules are commonly understood to be more explainable, due to
the local nature of the patterns involved. With numerical optimization methods
such as gradient boosting, ensembles of local decision rules can gain good
predictive performance on data involving global structure. Meanwhile, machine
learning models are being increasingly used to solve problems in high-stake
domains including healthcare and finance. Here, there is an emerging consensus
regarding the need for practitioners to understand whether and how those models
could perform robustly in the deployment environments, in the presence of
distributional shifts. Past research on local decision rules has focused mainly
on maximizing discriminant patterns, without due consideration of robustness
against distributional shifts. In order to fill this gap, we propose a new
method to learn and ensemble local decision rules, that are robust both in the
training and deployment environments. Specifically, we propose to leverage
causal knowledge by regarding the distributional shifts in subpopulations and
deployment environments as the results of interventions on the underlying
system. We propose two regularization terms based on causal knowledge to search
for optimal and stable rules. Experiments on both synthetic and benchmark
datasets show that our method is effective and robust against distributional
shifts in multiple environments.
