More than 20% of children will experience a traumatic event before they are 16 years old. Of those who experienced a trauma, a sizable minority will develop Posttraumatic Stress Disorder (PTSD), and other deleterious developmental, health, and psychiatric consequences (herein called Child Traumatic Stress). To diminish the considerable burden of traumatic stress on children and their families, the capacity to predict a child’s risk and to intervene to diminish this risk is extremely important. The literature on prediction of child traumatic stress from risk factors has yielded only modest results and - of those risk factors found to be predictive – it is difficult to determine which represent processes would lead to a diminution of risk, if effective intervention were applied. Almost certainly, traumatic stress results from a complex set of interacting bio-behavioral and social environmental processes, unfolding in specific ways over the course of development, and related to specific aspects of the traumatic exposure. Our project aims to apply state-of-the-art Machine Learning predictive modeling methods with a wide array of risk variables from the ALSPAC data set to generate reliable and accurate predictive models of PTSD and other child traumatic stress outcomes. We also aim to apply advanced non-experimental causal discovery algorithms to discover potentially remediable processes leading to traumatic stress outcomes that may reveal new opportunities for preventative intervention.