  From doctors diagnosing patients to judges setting bail, experts often base
their decisions on experience and intuition rather than on statistical models.
While understandable, relying on intuition over models has often been found to
result in inferior outcomes. Here we present a new method,
select-regress-and-round, for constructing simple rules that perform well for
complex decisions. These rules take the form of a weighted checklist, can be
applied mentally, and nonetheless rival the performance of modern machine
learning algorithms. Our method for creating these rules is itself simple, and
can be carried out by practitioners with basic statistics knowledge. We
demonstrate this technique with a detailed case study of judicial decisions to
release or detain defendants while they await trial. In this application, as in
many policy settings, the effects of proposed decision rules cannot be directly
observed from historical data: if a rule recommends releasing a defendant that
the judge in reality detained, we do not observe what would have happened under
the proposed action. We address this key counterfactual estimation problem by
drawing on tools from causal inference. We find that simple rules significantly
outperform judges and are on par with decisions derived from random forests
trained on all available features. Generalizing to 22 varied decision-making
domains, we find this basic result replicates. We conclude with an analytical
framework that helps explain why these simple decision rules perform as well as
they do.
