Title: Learning Interpretable Models with Causal Guarantees,
Abstract: Machine learning has shown much promise in helping improve the quality of
medical, legal, and economic decision-making. In these applications, machine
learning models must satisfy two important criteria: (i) they must be causal,
since the goal is typically to predict individual treatment effects, and (ii)
they must be interpretable, so that human decision makers can validate and
trust the model predictions. There has recently been much progress along each
direction independently, yet the state-of-the-art approaches are fundamentally
incompatible. We propose a framework for learning causal interpretable
models---from observational data---that can be used to predict individual
treatment effects. Our framework can be used with any algorithm for learning
interpretable models. Furthermore, we prove an error bound on the treatment
effects predicted by our model. Finally, in an experiment on real-world data,
we show that the models trained using our framework significantly outperform a
number of baselines.