  The optimal dynamic treatment rule (ODTR) framework offers an approach for
understanding which kinds of patients respond best to specific treatments -- in
other words, treatment effect heterogeneity. Recently, there has been a
proliferation of methods for estimating the ODTR. One such method is an
extension of the SuperLearner algorithm -- an ensemble method to optimally
combine candidate algorithms extensively used in prediction problems -- to
ODTRs. Following the "causal roadmap," we causally and statistically define the
ODTR and provide an introduction to estimating it using the ODTR SuperLearner.
Additionally, we highlight practical choices when implementing the algorithm,
including choice of candidate algorithms, metalearners to combine the
candidates, and risk functions to select the best combination of algorithms.
Using simulations, we illustrate how estimating the ODTR using this
SuperLearner approach can uncover treatment effect heterogeneity more
effectively than traditional approaches based on fitting a parametric
regression of the outcome on the treatment, covariates and treatment-covariate
interactions. We investigate the implications of choices in implementing an
ODTR SuperLearner at various sample sizes. Our results show the advantages of:
(1) including a combination of both flexible machine learning algorithms and
simple parametric estimators in the library of candidate algorithms; (2) using
an ensemble metalearner to combine candidates rather than selecting only the
best-performing candidate; (3) using the mean outcome under the rule as a risk
function. Finally, we apply the ODTR SuperLearner to the "Interventions" study,
an ongoing randomized controlled trial, to identify which justice-involved
adults with mental illness benefit most from cognitive behavioral therapy (CBT)
to reduce criminal re-offending.
