  We introduce Matched Machine Learning, a framework that combines the
flexibility of machine learning black boxes with the interpretability of
matching, a longstanding tool in observational causal inference.
Interpretability is paramount in many high-stakes application of causal
inference. Current tools for nonparametric estimation of both average and
individualized treatment effects are black-boxes that do not allow for human
auditing of estimates. Our framework uses machine learning to learn an optimal
metric for matching units and estimating outcomes, thus achieving the
performance of machine learning black-boxes, while being interpretable. Our
general framework encompasses several published works as special cases. We
provide asymptotic inference theory for our proposed framework, enabling users
to construct approximate confidence intervals around estimates of both
individualized and average treatment effects. We show empirically that
instances of Matched Machine Learning perform on par with black-box machine
learning methods and better than existing matching methods for similar
problems. Finally, in our application we show how Matched Machine Learning can
be used to perform causal inference even when covariate data are highly
complex: we study an image dataset, and produce high quality matches and
estimates of treatment effects.
