Berkeley Computational Social Science Forum
Date: Tuesday, November 30, 2021
Time: 4:00-5:00 PM Pacific Time
Location: Virtual Participation – Register to attend via Zoom
Optimal Dynamic Treatment Rule Estimation and Evaluation with Application to Criminal Justice Interventions in the United States
Lina Montoya, Postdoctoral Research Associate in Biostatistics at the University of North Carolina, Chapel Hill, and the University of California, Berkeley
Abstract: After collecting trial data, it may be of interest to understand treatment effect heterogeneity, i.e., answer the question: which intervention works best for whom? The optimal dynamic treatment rule (ODTR) framework offers an approach for understanding which kinds of patients respond best to specific treatments. 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 different estimators, such as cross-validated targeted maximum likelihood estimation. In addition, we show its finite-sample performance under various settings. We apply the ODTR SuperLearner to the "Interventions" study, an RCT 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.
The Computational Social Science Forum is an informal setting for the interdisciplinary exchange of ideas and scholarship at the intersection of social science and data science. Participants engage in a variety of activities such as presentations of work in progress, discussions and critiques of recent papers, introductions to new tools and methods, discussions around ethics, fairness, inequality, and responsible conduct of research, as well as professional development. This Forum is organized as part of the Computational Social Science Training Program, and weekly meetings are hosted by researchers from BIDS and D-Lab. The group welcomes social scientists and researchers with interests in data science methods and tools, and data scientists with applications or interests in public policy, social, behavioral, and health sciences. Participants include graduate students, postdocs, staff, and faculty, and members are encouraged to attend regularly in order to foster community around improving computational social science research, supporting the development and research of group members, and fostering new collaborations. Interested UC Berkeley community members are invited to use this registration form to receive the schedule and access links. Please contact email@example.com for more information or if you are interested in presenting current research for an upcoming session.
Lina Montoya is a postdoctoral researcher at the University of North Carolina, Chapel Hill (supervisor: Dr. Michael Kosorok) and the University of California, Berkeley (supervisor: Dr. Jennifer Skeem). Her methodological research is at the intersection of causal inference, statistics, and machine learning to develop ways of estimating, evaluating, and implementing optimal dynamic treatment rules, i.e., rules that determine which interventions work best for which people. Her applied research focuses on uncovering such heterogeneous treatment effects for 1) interventions aimed at increasing care retention among individuals with HIV in East Africa; and 2) interventions for reducing recidivism among justice-involved adults with mental illness in the United States.