This study explores the influences of education, intelligence, and income on mental disorders using Mendelian randomisation (MR) and multivariable MR (MVMR) analyses. The research evaluated the overall and independent causal effects of these three psychosocial factors on 13 common mental disorders. The study used genome-wide association study (GWAS) summary datasets, including educational attainment (EA), household income, and intelligence, with sample sizes ranging from 9907 to 807,553. The main method in the MR analysis was inverse-variance weighting. The results showed that higher EA was a protective factor for eight mental disorders but contributed to anorexia nervosa, obsessive-compulsive disorder (OCD), bipolar disorder (BD), and autism spectrum disorder (ASD). Higher intelligence was a protective factor for five mental disorders but a risk factor for OCD and ASD. Higher household income protected against 10 mental disorders but conferred risk for anorexia nervosa. The MVMR analysis revealed that higher EA was a direct protective factor for attention-deficit/hyperactivity disorder (ADHD) and insomnia but a direct risk factor for schizophrenia, BD, and ASD. Higher intelligence was a direct protective factor for schizophrenia but a direct risk factor for major depressive disorder (MDD) and ASD. Higher income was a direct protective factor for seven mental disorders, including schizophrenia, BD, MDD, ASD, post-traumatic stress disorder, ADHD, and anxiety disorder. The study highlights that education, intelligence, and income are intertwined, and their independent effects on mental disorders are more complex than their overall effects. The findings suggest that higher EA or higher intelligence may exert independent detrimental effects on some mental disorders, while high income is a direct protective factor for most common mental disorders. The study also emphasizes the importance of considering each psychosocial factor in clinical practice for mental disorders to provide additional granularity for disease-specific prevention and management.This study explores the influences of education, intelligence, and income on mental disorders using Mendelian randomisation (MR) and multivariable MR (MVMR) analyses. The research evaluated the overall and independent causal effects of these three psychosocial factors on 13 common mental disorders. The study used genome-wide association study (GWAS) summary datasets, including educational attainment (EA), household income, and intelligence, with sample sizes ranging from 9907 to 807,553. The main method in the MR analysis was inverse-variance weighting. The results showed that higher EA was a protective factor for eight mental disorders but contributed to anorexia nervosa, obsessive-compulsive disorder (OCD), bipolar disorder (BD), and autism spectrum disorder (ASD). Higher intelligence was a protective factor for five mental disorders but a risk factor for OCD and ASD. Higher household income protected against 10 mental disorders but conferred risk for anorexia nervosa. The MVMR analysis revealed that higher EA was a direct protective factor for attention-deficit/hyperactivity disorder (ADHD) and insomnia but a direct risk factor for schizophrenia, BD, and ASD. Higher intelligence was a direct protective factor for schizophrenia but a direct risk factor for major depressive disorder (MDD) and ASD. Higher income was a direct protective factor for seven mental disorders, including schizophrenia, BD, MDD, ASD, post-traumatic stress disorder, ADHD, and anxiety disorder. The study highlights that education, intelligence, and income are intertwined, and their independent effects on mental disorders are more complex than their overall effects. The findings suggest that higher EA or higher intelligence may exert independent detrimental effects on some mental disorders, while high income is a direct protective factor for most common mental disorders. The study also emphasizes the importance of considering each psychosocial factor in clinical practice for mental disorders to provide additional granularity for disease-specific prevention and management.