A Bayesian Structural Causal Modeling Framework for Analyzing Childhood Stunting Factors - Celia Sianipar; Adji Achmad Rinaldo Fernandes; Solimun Solimun; Septi Nafisa Ulluya Zahra; Fachira Haneinanda Junianto (JTAM (Jurnal Teori dan Aplikasi Matematika), 2026)
This quantitative study investigates the causal determinants of childhood stunting using a structured questionnaire as the primary research instrument. The analysis applies Bayesian path modeling to examine how Economic Level influences Child Nutritional Status both directly and indirectly through Children’s Diet. Bayesian estimation is used to obtain stable and reliable parameter values, with convergence checks ensuring model adequacy. The overly technical explanations of MCMC procedures and specific sampling algorithms from the original version are condensed to maintain clarity in an abstract. The results show that Children’s Diet plays a strong mediating role, indicating that economic improvements contribute more substantially to better nutritional outcomes when dietary practices are strengthened. These findings highlight clear policy implications, particularly the need to integrate dietary interventions with economic support programs. Overall, the study demonstrates that Bayesian path analysis provides a rigorous yet flexible approach for evaluating interconnected determinants of child nutrition and contributes evidence-based insights for stunting reduction strategies.