TY - JOUR
T1 - On the Detection of Population Heterogeneity in Causation Between Two Variables
T2 - Finite Mixture Modeling of Data Collected from Twin Pairs
AU - Vinh, Philip B.
AU - Verhulst, Brad
AU - Maes, Hermine H.M.
AU - Dolan, Conor V.
AU - Neale, Michael C.
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/11/26
Y1 - 2024/11/26
N2 - Causal inference is inherently complex and relies on key assumptions that can be difficult to validate. One strong assumption is population homogeneity, which assumes that the causal direction remains consistent across individuals. However, there may be variation in causal directions across subpopulations, leading to potential heterogeneity. In psychiatry, for example, the co-occurrence of disorders such as depression and substance use disorder can arise from multiple sources, including shared genetic or environmental factors (common causes) or direct causal pathways between the disorders. A patient diagnosed with two disorders might have one recognized as primary and the other as secondary, suggesting the existence of different types of comorbidity. For example, in some individuals, depression might lead to substance use, while in others, substance use could lead to depression. We account for potential heterogeneity in causal direction by integrating the Direction of Causation (DoC) model for twin data with finite mixture modeling, which allows for the calculation of individual-level likelihoods for alternate causal directions. Through simulations, we demonstrate the effectiveness of using the Direction of Causation Twin Mixture (mixDoC) model to detect and model heterogeneity due to varying causal directions.
AB - Causal inference is inherently complex and relies on key assumptions that can be difficult to validate. One strong assumption is population homogeneity, which assumes that the causal direction remains consistent across individuals. However, there may be variation in causal directions across subpopulations, leading to potential heterogeneity. In psychiatry, for example, the co-occurrence of disorders such as depression and substance use disorder can arise from multiple sources, including shared genetic or environmental factors (common causes) or direct causal pathways between the disorders. A patient diagnosed with two disorders might have one recognized as primary and the other as secondary, suggesting the existence of different types of comorbidity. For example, in some individuals, depression might lead to substance use, while in others, substance use could lead to depression. We account for potential heterogeneity in causal direction by integrating the Direction of Causation (DoC) model for twin data with finite mixture modeling, which allows for the calculation of individual-level likelihoods for alternate causal directions. Through simulations, we demonstrate the effectiveness of using the Direction of Causation Twin Mixture (mixDoC) model to detect and model heterogeneity due to varying causal directions.
KW - Causality
KW - Mixture modeling
KW - Statistical modeling
KW - Twin design
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U2 - 10.1007/s10519-024-10207-9
DO - 10.1007/s10519-024-10207-9
M3 - Article
AN - SCOPUS:85210377701
SN - 0001-8244
JO - Behavior Genetics
JF - Behavior Genetics
ER -