# Computational Models for the Prediction and Prevention of Child Traumatic Stress - Resubmission - 1

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2022 · $621,125

## Abstract

Project Summary/Abstract
At least 40% of children will experience a traumatic event. Of those who experience a trauma, 15-40% will
develop Posttraumatic Stress Disorder (PTSD), and other adverse psychiatric, health, and functional outcomes
(herein called Child Traumatic Stress - CTS). Despite decades of research on risk factors for CTS, the field has
not arrived at specific risk factor models that can accurately predict the likelihood of CTS outcomes or identify
factors that – if changed – would change their likelihood. Knowledge about changes in factors that result in
changes in outcomes is, by definition, causal. The vast majority of findings in the literature on risk for CTS
cannot provide such causal knowledge because such findings were based on the application of correlational
methods to observational data. Experimental research cannot – for all practical purposes - be conducted for
human research on risk for CTS. Thus, the field is left with correlational observational research as the near
exclusive generator of empirical knowledge on risk for CTS, and such knowledge is unsuitable to guide the
actions (i.e. interventions) that must be taken to change children's likelihood of acquiring CTS outcomes. We
propose to address this considerable barrier to progress by applying methods that can enable confident causal
inference with large observational data sets containing a broad diversity of risk variables for CTS. Machine
Learning (ML) predictive and causal modeling methods will be applied to discover causal relationships among
measured variables from observational data: and from such determined causal relationships, to estimate the
effect on a CTS outcome when a causal variable is manipulated (i.e. intervention simulation). We will build
models for outcomes associated with childhood trauma in the literature and that entail significant burden to
children's well-being, functioning, and development: PTSD, Depression, Substance Abuse, Health, and
Educational Performance.

## Key facts

- **NIH application ID:** 10455072
- **Project number:** 5R01MH119114-04
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** GLENN N SAXE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $621,125
- **Award type:** 5
- **Project period:** 2019-09-20 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10455072

## Citation

> US National Institutes of Health, RePORTER application 10455072, Computational Models for the Prediction and Prevention of Child Traumatic Stress - Resubmission - 1 (5R01MH119114-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10455072. Licensed CC0.

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