Is Reading an Upstream Predictor of Science and Mathematics Achievements in PISA? A Bayesian Network Analysis for Policy Educational Interventions on Socio‐Economic Dispersion and Gender Gaps

This study investigates the structural relationships among reading, science and mathematics performance, socio‐economic dispersion (SES_Variance) and gender gaps using cross‐country PISA 2022 data. The primary objective is to assess whether reading functions as an upstream determinant of academic achievement and to evaluate the causal roles of SES and gender disparities within an integrated probabilistic framework. We employ Bayesian network (BN) modelling with both constraint‐based (Peter–Clark) and score‐based (hill‐climbing with BDeu score) algorithms under multiple discretization schemes, complemented by ANOVA analyses. Policy‐relevant interventions are simulated using do‐operator logic to examine SES and gender gap equalization scenarios. The BN also supports probabilistic queries that define performance archetypes. Robustness is assessed through alternative binning strategies and bootstrap stability analysis. The BN reveals a stable performance chain (Reading → Science → Math) and a direct influence of SES_Variance on all subjects. Contrary to econometric associations, gender gaps exhibit no consistent direct causal effect on performance, except for a discretization‐sensitive link from GenderGap_Math to Mean_Science_2022 in 4‐bin models. SES equalization simulations produce counter‐intuitive reductions in high‐performance probabilities.