Methodological Advances in the Study of Hidden Variables: A Demonstration on Clinical Alcohol Use Disorder Data

Research in the domain of psychopathology has been hindered by hidden variables—variables that are important to understanding and treating psychopathological illnesses but are unmeasured. Recent methodological advances in machine learning have culminated in the ability to discover and identify the influence of hidden variables that confound the observed relationships among measured variables. We apply a combination of traditional methods and more recent advances to a data set of alcohol use disorder patients with comorbid internalizing disorders, and find that the increasingly advanced methods produce increasingly informative and reliable results. These results include novel findings evaluated positively by our psychopathologists, as well as findings validated with knowledge from existing literature. We also find that advanced graph discovery methods can guide the use of latent variable modeling procedures, which can in turn explain the output of the graph discovery methods, resulting in a synergistic relationship between two seemingly distinct classes of methods