  Our goal is to produce methods for observational causal inference that are
auditable, easy to troubleshoot, accurate for treatment effect estimation, and
scalable to high-dimensional data. We describe a general framework called
Model-to-Match that achieves these goals by (i) learning a distance metric via
outcome modeling, (ii) creating matched groups using the distance metric, and
(iii) using the matched groups to estimate treatment effects. Model-to-Match
uses variable importance measurements to construct a distance metric, making it
a flexible framework that can be adapted to various applications. Concentrating
on the scalability of the problem in the number of potential confounders, we
operationalize the Model-to-Match framework with LASSO. We derive performance
guarantees for settings where LASSO outcome modeling consistently identifies
all confounders (importantly without requiring the linear model to be correctly
specified). We also provide experimental results demonstrating the method's
auditability, accuracy, and scalability as well as extensions to more general
nonparametric outcome modeling.
