# Data integration for causal inference in behavioral health

> **NIH NIH T32** · JOHNS HOPKINS UNIVERSITY · 2024 · $263,960

## Abstract

Behavioral health, broadly defined to include mental health and substance use, includes many of the most
pressing public health problems of our time. The transition to a data-rich, web-interconnected society has
generated an opportunity to generate solutions, but it necessitates a paradigm shift in workforce training in
data analytics. The goal of this training program is to train scholars to become leaders in the use of advanced
computational methods and designs to estimate causal effects in behavioral health. To accomplish this goal,
we will provide rigorous training and high-quality mentorship in: 1) the science of behavioral health; 2)
computational and analytic tools to manage, analyze, and integrate complex data sources; and 3) causal
inference methods to take full advantage of these data. Trainees will receive interdisciplinary team-based
training and will acquire a deep understanding of all three areas. This training program will capitalize on the
rich resources for behavioral health, analytic and computational methods, and biostatistics at the Johns
Hopkins Bloomberg School of Public Health (JHSPH) and the broader University. The program will be housed
in the Department of Mental Health but the 5 trainees per year will come from any of the four social science
oriented departments at JHSPH: 1) Mental Health; 2) Health Behavior & Society; 3) Health Policy &
Management; and 4) Population Family & Reproductive Health. Further, the training grant will leverage close
connections with data scientists, statisticians, and computer scientists from across the University. Trainees will
obtain the skills and experiences needed to lead multi-disciplinary, collaborative research teams. Trainees will
undertake a rigorous program of coursework in the core domains of public health and behavioral health
including behavioral and social science, epidemiology, biostatistics, data science, population health
informatics, causal inference, and research ethics. In addition, each trainee will take additional elective courses
in social and behavioral perspectives on mental health and substance use, informatics and computational skills,
and causal and statistical inference. Trainees will participate in a year-long seminar on analytics for behavioral
health, a bi-weekly seminar to discuss research in progress and professional development, ongoing mentored
research projects, and integrative activities to complement their didactic curriculum. The focus area of the
program builds on strengths within JHSPH; these areas also are highlighted as priorities by OBSSR, NIMH,
and NIDA. The trainees will be supported by an experienced group of 21 core faculty and each trainee will be
co-advised by one of 9 affiliated faculty with methodological expertise. The training program director, Dr.
Elizabeth Stuart, is a national leader in analytic tools for behavioral health, and will be supported by a 4-
member internal Executive Committee and a 5-member external Advisory Committee. Th...

## Key facts

- **NIH application ID:** 10850945
- **Project number:** 5T32MH122357-05
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Elizabeth A. Stuart
- **Activity code:** T32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $263,960
- **Award type:** 5
- **Project period:** 2020-07-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10850945

## Citation

> US National Institutes of Health, RePORTER application 10850945, Data integration for causal inference in behavioral health (5T32MH122357-05). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10850945. Licensed CC0.

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