DOI: 10.15154/1524272
title: Statistical causal inference approaches with the ABCD dataset

description: Researchers studying human behavior are often interested in answering causal research questions like, what effect does a drug exposure or environmental feature have on an outcome measure related to the brain or mental health. Prior studies attempting to answer these questions have often used small samples and cross-sectional designs, which make it challenging, if not impossible, to understand the causal nature of these relationships. Furthermore, the use of experimental designs like randomized controlled trials are practically impossible to implement with illicit drugs (e.g., cannabis) and vulnerable populations (e.g., children or pregnant individuals). One way to navigate these challenges is to use statistical causal inference methods applied to large observational studies, like the Adolescent Brain and Cognitive Development study ("ABCD"; N=11,873). Broadly, statistical causal inference methods seek to leverage a dataset to approximate an experimental design. Techniques like a causal random forest (CRF) model infers an "average treatment effect" of an “exposure” or “treatment” on an outcome measure by using decision trees to compare similar subgroups of participants matched on variables that would have otherwise confounded a causal relationship. CRFs also identify "heterogeneous treatment effects" by estimating moderators between the exposure and outcome measure. Quasi-experimental methods like regression discontinuity designs (RDD) infers causality by estimating a discontinuity in an outcome measure between participants who are close to a "threshold" for their inclusion vs. exclusion from an “exposure” or “treatment”.  

Using a causal inference framework, this project seeks to identify modifiable factors (e.g., sleep, extracurriculars, screen media hours) that are causally related to brain and behavioral health outcome measures (e.g., internalizing disorders, brain activation measures). For example, a CRF model will test the hypothesis that there is an average treatment effect of sleep hours on internalizing symptoms, and this causal pathway might be moderated by prenatal or adolescent cannabis exposure. Moreover, an RDD model will test the hypothesis that living within 250mi. of a time-zone boundary (i.e., the discontinuity threshold) will have an effect on cognitive or other psychosocial measures. This is hypothesized due to the discrepancy between sunlight exposure and clock-time that is exaggerated near time-zone boundaries (early sunsets just west vs. late sunsets just east of a boundary), yet social responsibilities (school start times) are constant across boundaries ("social jetlag"). Similar methods will also be pursued with other exposure/treatment measures, and outcome measures, pertaining adolescent brain and behavioral health. 

By illuminating causal factors like these, one can begin to develop explanatory models and novel interventions aimed at promoting adolescent mental health and wellbeing. The use of a causal framework is key, as the modification of causal processes are most likely to have an effect on critical outcome measures