Optimal Dynamic Treatment Rule Estimation and Evaluation with Application to Criminal Justice Interventions in the United States

The optimal dynamic treatment rule (ODTR) framework offers an approach for understanding which kinds of individuals respond best to specific interventions. 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," in this talk we causally and statistically define the ODTR, and different parameters to evaluate it. We show how to estimate the ODTR with SuperLearner and evaluate it using cross-validated targeted maximum likelihood estimation. We apply the ODTR SuperLearner to the "Interventions" study, a randomized trial that is currently underway aimed at reducing recidivism among justice-involved adults with mental illness in the United States. Specifically, we show preliminary results for the ODTR SuperLearner applied to this data, which aims to learn for whom Cognitive Behavioral Therapy (CBT) treatment works best to reduce recidivism, instead of Treatment As Usual (TAU; psychiatric services). This is joint work with Drs. Maya Petersen, Mark van der Laan, and Jennifer Skeem.