Power Analysis for Causal Discovery.

Causal discovery algorithms have the potential to impact many fields of science. However, substantial foundational work on the statistical properties of causal discovery algorithms is still needed. This paper presents what is to our knowledge the first method for conducting power analysis for causal discovery algorithms.

Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees.

Learning graphical conditional independence structures is an important machine learning problem and a cornerstone of causal discovery. However, the accuracy and execution time of learning algorithms generally struggle to scale to problems with hundreds of highly connected variables-for instance, recovering brain networks from fMRI data. We introduce the best order score search (BOSS) and grow-shrink trees (GSTs) for learning directed acyclic graphs (DAGs) in this paradigm.

Identifying psychological predictors of SARS-CoV-2 vaccination: A machine learning study.

Background: Major barriers to addressing SARS-CoV-2 vaccine hesitancy include limited knowledge of what causes delay/refusal of SARS-CoV-2 vaccination and limited ability to predict who will remain unvaccinated over significant time periods despite vaccine availability. The present study begins to address these barriers by developing a machine learning model that prospectively predicts who will persist in not vaccinating against SARS-CoV-2.

Method: Unvaccinated individuals (n = 325) who completed a baseline survey were followed over the six-month period when vaccines against SARS-CoV-2 were first widely available (April-October 2021).

Beyond integrative experiment design: Systematic experimentation guided by causal discovery AI.

Integrative experiment design is a needed improvement over ad hoc experiments, but the specific proposed method has limitations. We urge a further break with tradition through the use of an enormous untapped resource: Decades of causal discovery artificial intelligence (AI) literature on optimizing the design of systematic experimentation.

Causal Discovery for fMRI data: Challenges, Solutions, and a Case Study.

Designing studies that apply causal discovery requires navigating many researcher degrees of freedom. This complexity is exacerbated when the study involves fMRI data. In this paper we (i) describe nine challenges that occur when applying causal discovery to fMRI data, (ii) discuss the space of decisions that need to be made, (iii) review how a recent case study made those decisions, (iv) and identify existing gaps that could potentially be solved by the development of new methods.

Integrating Causal Discovery and Clinically-Relevant Insights to Explore Directional Relationships between Autistic Features, Sex at Birth, and Cognitive Abilities.

Prevalence in autism spectrum disorder (ASD) diagnosis has long been strongly male-biased. Yet, consensus has not been reached on mechanisms and clinical features that underlie sex-based discrepancies. Whereas females may be under-diagnosed because of inconsistencies in diagnostic/ascertainment procedures (sex-biased criteria, social camouflaging), diagnosed males may have exhibited more overt behaviors (e.

Causal discovery analysis: A promising tool in advancing precision medicine for eating disorders.

Objective: Precision medicine (i.e., individually tailored treatments) represents an optimal goal for treating complex psychiatric disorders, including eating disorders.

A Computational Framework for EEG Causal Oscillatory Connectivity.

Here we advance a new approach for measuring EEG causal oscillatory connectivity, capitalizing on recent advances in causal discovery analysis for skewed time series data and in spectral parameterization of time-frequency (TF) data. We first parameterize EEG TF data into separate oscillatory and aperiodic components. We then measure causal interactions between separated oscillatory data with the recently proposed causal connectivity method Greedy Adjacencies and Non-Gaussian Orientations (GANGO).

Frontal tDCS reduces alcohol relapse rates by increasing connections from left dorsolateral prefrontal cortex to addiction networks.

Background: Brain-based interventions are needed to address persistent relapse in alcohol use disorder (AUD). Neuroimaging evidence suggests higher frontal connectivity as well as higher within-network connectivity of theoretically defined addiction networks are associated with reduced relapse rates and extended abstinence during follow-up periods.

Objective: /Hypothesis: A longitudinal randomized double-blind sham-controlled clinical trial investigated whether a non-invasive neuromodulation intervention delivered during early abstinence can (i) modulate connectivity of addiction networks supporting abstinence and (ii) improve relapse rates.

Causal pathways to social and occupational functioning in the first episode of schizophrenia: uncovering unmet treatment needs.

Background: We aimed to identify unmet treatment needs for improving social and occupational functioning in early schizophrenia using a data-driven causal discovery analysis.

Methods: Demographic, clinical, and psychosocial measures were obtained for 276 participants from the Recovery After an Initial Schizophrenia Episode Early Treatment Program (RAISE-ETP) trial at baseline and 6-months, along with measures of social and occupational functioning from the Quality of Life Scale. The Greedy Fast Causal Inference algorithm was used to learn a partial ancestral graph modeling causal relationships across baseline variables and 6-month functioning.

Data-Driven Analytics to Discover APRN's Impact on Nursing Home Hospitalization: Causal Discovery Analysis.

Objectives: Research shows advanced practice registered nurses (APRNs) embedded in nursing homes (NHs) reduce resident hospitalizations. However, the specific APRN activities that reduce hospitalizations have not been adequately investigated. This study aims to identify the causal links between APRN activities and NHs resident hospitalization.

One data set, many analysts: Implications for practicing scientists.

Researchers routinely face choices throughout the data analysis process. It is often opaque to readers how these choices are made, how they affect the findings, and whether or not data analysis results are unduly influenced by subjective decisions. This concern is spurring numerous investigations into the variability of data analysis results.

Causal discovery replicates symptomatic and functional interrelations of posttraumatic stress across five patient populations.

Introduction: Approximately half of individuals with posttraumatic stress disorder (PTSD) may meet criteria for other psychiatric disorders, and PTSD symptoms are associated with diminished health and psychosocial functioning. However, few studies examine the longitudinal progression of PTSD symptoms concurrent with related symptom domains and functional outcomes, such that may neglect important longitudinal patterns of symptom progression beyond PTSD specifically.

Methods: Therefore, we used longitudinal causal discovery analysis to examine the longitudinal interrelations among PTSD symptoms, depressive symptoms, substance abuse, and various other domains of functioning in five longitudinal cohorts representing veterans ( = 241), civilians seeking treatment for anxiety disorders ( = 79), civilian women seeking treatment for post-traumatic stress and substance abuse ( = 116), active duty military members assessed 0-90 days following TBI ( = 243), and civilians with a history of TBI ( = 43).

Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder.

Cannabis Use Disorder (CUD) has been linked to a complex set of neuro-behavioral risk factors. While many studies have revealed sex and gender differences, the relative importance of these risk factors by sex and gender has not been described. We used an "explainable" machine learning approach that combined decision trees [gradient tree boosting, XGBoost] with factor ranking tools [SHapley's Additive exPlanations (SHAP)] to investigate sex and gender differences in CUD.

Quantifying heterogeneity in mood-alcohol relationships with idiographic causal models.

Background: Ecological momentary assessment (EMA) studies have provided conflicting evidence for the mood regulation tenet that people drink in response to positive and negative moods. The current study examined mood-to-alcohol relationships idiographically to quantify the prevalence and intensity of relationships between positive and negative moods and drinking across individuals.

Method: We used two EMA samples: 96 heavy drinking college students (sample 1) and 19 young adults completing an ecological momentary intervention (EMI) for drinking to cope (sample 2).

Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study.

Purpose: To conduct a prospective observational study across 12 U.S. hospitals to evaluate real-time performance of an interpretable artificial intelligence (AI) model to detect COVID-19 on chest radiographs.

The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks.

We demonstrate a data-driven approach for calculating a "causal connectome" of directed connectivity from resting-state fMRI data using a greedy adjacency search and pairwise non-Gaussian edge orientations. We used this approach to construct n = 442 causal connectomes. These connectomes were very sparse in comparison to typical Pearson correlation-based graphs (roughly 2.

Biased and inflexible interpretations of ambiguous social situations: Associations with eating disorder symptoms and socioemotional functioning.

Background: Research indicates that difficulties across multiple socioemotional functioning domains (e.g., social emotion expression/regulation, response to social elicitors of emotion) and negatively biased interpretations of ambiguous social situations may affect eating disorder symptoms.

Willingness to vaccinate against SARS-CoV-2: The role of reasoning biases and conspiracist ideation.

Unlabelled: BACKGR1OUND: Widespread vaccine hesitancy and refusal complicate containment of the SARS-CoV-2 pandemic. Extant research indicates that biased reasoning and conspiracist ideation discourage vaccination. However, causal pathways from these constructs to vaccine hesitancy and refusal remain underspecified, impeding efforts to intervene and increase vaccine uptake.

Heterogeneity in COVID-19 Patients at Multiple Levels of Granularity: From Biclusters to Clinical Interventions.

Several studies have shown that COVID-19 patients with prior comorbidities have a higher risk for adverse outcomes, resulting in a disproportionate impact on older adults and minorities that fit that profile. However, although there is considerable heterogeneity in the comorbidity profiles of these populations, not much is known about how prior comorbidities co-occur to form COVID-19 patient subgroups, and their implications for targeted care. Here we used bipartite networks to quantitatively and visually analyze heterogeneity in the comorbidity profiles of COVID-19 inpatients, based on electronic health records from 12 hospitals and 60 clinics in the greater Minneapolis region.

A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals.

Importance: An artificial intelligence (AI)-based model to predict COVID-19 likelihood from chest x-ray (CXR) findings can serve as an important adjunct to accelerate immediate clinical decision making and improve clinical decision making. Despite significant efforts, many limitations and biases exist in previously developed AI diagnostic models for COVID-19. Utilizing a large set of local and international CXR images, we developed an AI model with high performance on temporal and external validation.

Neighborhood characteristics and psychiatric disorders in the aftermath of mass trauma: A representative study of New York City public school 4th-12th graders after 9/11.

Studies of the relationship between neighborhood characteristics and childhood/adolescent psychopathology in large samples examined one outcome only, and/or general (e.g., 'psychological distress') or aggregate (e.

A simple interpretation of undirected edges in essential graphs is wrong.

Artificial intelligence for causal discovery frequently uses Markov equivalence classes of directed acyclic graphs, graphically represented as essential graphs, as a way of representing uncertainty in causal directionality. There has been confusion regarding how to interpret undirected edges in essential graphs, however. In particular, experts and non-experts both have difficulty quantifying the likelihood of uncertain causal arrows being pointed in one direction or another.

An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis.

Alcohol use disorder (AUD) has high prevalence and adverse societal impacts, but our understanding of the factors driving AUD is hampered by a lack of studies that describe the complex neurobehavioral mechanisms driving AUD. We analyzed causal pathways to AUD severity using Causal Discovery Analysis (CDA) with data from the Human Connectome Project (HCP; n = 926 [54% female], 22% AUD [37% female]). We applied exploratory factor analysis to parse the wide HCP phenotypic space (100 measures) into 18 underlying domains, and we assessed functional connectivity within 12 resting-state brain networks.