While there have been major advances in documenting the consequences of childhood adversities for psychopathology, Professor Danese's excellent paper challenges existing theory and research methods, urging the field to move ahead with future research that overcomes existing limitations. Importantly, he reminds us of the methodological caveats necessary to consider when assessing the body of evidence for causal effects of childhood trauma and urges caution in interpreting the ACEs literature. This editorial calls attention to and elaborates on a number of issues, including (a) why prospective and retrospective data cannot be used interchangeably; (b) the need for researchers to distinguish among childhood adversities, childhood traumas, and childhood maltreatment; (c) the sparse attention at present to the role of pre-existing vulnerabilities in influencing assessments of the risk of psychopathology; and (d) the critical importance of contextual factors (e.g., age, sex, race, ethnicity, and social class) that are likely to influence the risk of psychopathology. Professor Danese argues for the use of new analytic strategies to advance the field. This editorial elaborates on this recommendation and calls attention to the use of machine learning techniques that may be particularly worthwhile for the child maltreatment field, where there is little psychometric research on measures.
(Publisher abstract provided.)