B4096 - Center for Causal Data Science for Child Maltreatment Prevention - 27/06/2022

B number: 
Principal applicant name: 
Glenn Saxe | NYU Langone (USA)
Constantin Aliferis, MD, PhD, FACMI, Sisi Ma, PhD, Thomas Kirsh, BS, Linmin Wang, MS, Michelle Papp
Title of project: 
Center for Causal Data Science for Child Maltreatment Prevention
Proposal summary: 

Maltreatment by caregivers is associated with devastating consequences; include poor school performance, mental disorders, substance abuse, violence and suicide, and chronic health problems. Despite decades of research to acquire the scientific knowledge to prevent maltreatment exposures (ME’s) and maltreatment-related outcomes (MRO’s); only a small proportion of those children at risk have benefited. Complex etiology entails several requirements for scientific knowledge to achieve preventative results for a large proportion of children at risk: (1) etiology must be accurately determined (at the approximate level of its’ complexity), because intervention targeting non-etiological/non-causal factors cannot result in reduced risk, (2) valid models of complex etiology must be used to enable unbiased estimates of quantitative causal effect, (3) intervention targets must be identified from those factors with largest causal effect, and (4) personalized and precise intervention strategies must be determined from those intervention targets. We propose to address these considerable barriers to progress, by establishing the Center on Causal Data Science for Child and Adolescent Maltreatment Prevention (The CHAMP Center), to (1) discover the scientific knowledge on the complex etiology of the ME’s and MRO’s, by applying state-of-the -art methods of Causal Data Science to several large, and highly relevant existing data sets; (2) translate this knowledge into specific Decision Support Tools for those practitioners, whose decisions – when supported by these tools – can result in large-scale prevention, and (3) validate this knowledge in the field, in trials using the developed Decision Support Tools, with those practitioners, and the children and families whom they serve.

Impact of research: 
To create reliable, accurate and interpretable predictive and causal models of ME and MROs that can guide future research and clinical care. To use our research findings to develop decision support tools that can guide personalized and precise intervention strategies.
Date proposal received: 
Friday, 24 June, 2022
Date proposal approved: 
Monday, 27 June, 2022
Statistics/methodology, Behaviour - e.g. antisocial behaviour, risk behaviour, etc., Computer simulations/modelling/algorithms, Statistical methods, Childhood - childcare, childhood adversity, Genetics, Methods - e.g. cross cohort analysis, data mining, mendelian randomisation, etc., Psychology - personality