  Practitioners in medicine, business, political science, and other fields are
increasingly aware that decisions should be personalized to each patient,
customer, or voter. A given treatment (e.g. a drug or advertisement) should be
administered only to those who will respond most positively, and certainly not
to those who will be harmed by it. Individual-level treatment effects can be
estimated with tools adapted from machine learning, but different models can
yield contradictory estimates. Unlike risk prediction models, however,
treatment effect models cannot be easily evaluated against each other using a
held-out test set because the true treatment effect itself is never directly
observed. Besides outcome prediction accuracy, several metrics that can
leverage held-out data to evaluate treatment effects models have been proposed,
but they are not widely used. We provide a didactic framework that elucidates
the relationships between the different approaches and compare them all using a
variety of simulations of both randomized and observational data. Our results
show that researchers estimating heterogenous treatment effects need not limit
themselves to a single model-fitting algorithm. Instead of relying on a single
method, multiple models fit by a diverse set of algorithms should be evaluated
against each other using an objective function learned from the validation set.
The model minimizing that objective should be used for estimating the
individual treatment effect for future individuals.
