2022/11/02 by Luís Fernando Silva Castro-de-Araujo, Luis F. S. Castro-de-Araujo, Madhurbain Singh +5 · 34 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · Psychology · #Advanced Causal Inference Techniques #Artificial intelligence #Biology #Causal inference #Causal model #Causality (physics) #Causation #Cognitive Abilities and Testing #Computer science #Confounding #Developmental psychology #Econometrics #Genetic Associations and Epidemiology #Genetic variants #Genetics #Inference #Instrumental variable #Mathematics #Mendelian randomization #Psychology #Statistics #Trait
paper · pdf · doi:10.1007/s10519-022-10122-x
published in Behavior Genetics 53(1), 63-73 (Springer Science+Business Media)
openalex publication_date 2022/11/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Establishing causality is an essential step towards developing interventions for psychiatric disorders, substance use and many other conditions. While randomized controlled trials (RCTs) are considered the gold standard for causal inference, they are unethical in many scenarios. Mendelian randomization (MR) can be used in such cases, but importantly both RCTs and MR assume unidirectional causality. In this paper, we developed a new model, MRDoC2, that can be used to identify bidirectional causation in the presence of confounding due to both familial and non-familial sources. Our model extends the MRDoC model (Minică et al. in Behav Genet 48:337-349, 10.1007/s10519-018-9904-4 , 2018), by simultaneously including risk scores for each trait. Furthermore, the power to detect causal effects in MRDoC2 does not require the phenotypes to have different additive genetic or shared environmental sources of variance, as is the case in the direction of causation twin model (Heath et al. in Behav Genet 23:29-50, 10.1007/BF01067552 , 1993).