  When devising a course of treatment for a patient, doctors often have little
quantitative evidence on which to base their decisions, beyond their medical
education and published clinical trials. Stanford Health Care alone has
millions of electronic medical records (EMRs) that are only just recently being
leveraged to inform better treatment recommendations. These data present a
unique challenge because they are high-dimensional and observational. Our goal
is to make personalized treatment recommendations based on the outcomes for
past patients similar to a new patient. We propose and analyze three methods
for estimating heterogeneous treatment effects using observational data. Our
methods perform well in simulations using a wide variety of treatment effect
functions, and we present results of applying the two most promising methods to
data from The SPRINT Data Analysis Challenge, from a large randomized trial of
a treatment for high blood pressure.
