FairEdu-GCT: A Graph Enhanced, Fairness Aware Framework for Predicting Heterogeneous Returns to Higher Education

How much a college education pays off varies widely from one student to the next, and that variation matters for admissions, financial aid, and mobility policy. Most estimates, however, report a single average return and treat each institution as an isolated row in a table, ignoring how schools relate to one another and how outcomes are distributed across demographic groups. We present FairEdu-GCT(Graph-Enhanced Causal Transformer), a framework that couples a heterogeneous graph encoder with a Transformer sequence model and an explicit fairness penalty. Institutions, geographic regions, and academic disciplines form a typed graph whose edges record graduate flows, spatial proximity, and disciplinary overlap. A Relational Graph Attention Network (R-GAT) turns this structure into institutional ecosystem embeddingsthat carry peer effects, regional labor-market signals, and the spread of institutional prestige, none of which survives in tabular representations. A Transformer then encodes each student’s educational history and merges it with the institutional embedding through a cross-modal attention bridge, and a counterfactual decoding head returns the full conditional earnings distribution under alternative institutional choices rather than a single point estimate. Because students are not randomly assigned to schools, we make no claim of strict causal identification; we treat CATE and PEHE strictly as estimation-quality diagnostics for a confounding-adjusted contrast, not as evidence of a proven causal effect. We instead adjust for observed confounders through a doubly robust objective and add an equalized opportunity regularizer so that accuracy does not come at the expense of protected subgroups. On linked U.S. College Scorecard, IPEDS, and NLSY97 data (6256 institutions drawn from 7312 Title IV schools; 161,043 person-institution-year records from 8984 respondents followed for ten years), FairEdu-GCT lowers RMSE by 16.2% and Precision in Estimation of Heterogeneous Effects (PEHE) by 25.1% against the strongest baseline (Causal Forest, DragonNet, TARNet, and TabTransformer), and shrinks the demographic parity gap by 46.5%. All gains are reported with 95% confidence intervals and effect sizes over ten seeds, and an extended fairness audit (calibration, predictive parity, and subgroup robustness) confirms the improvement is not confined to the two metrics we optimize. Ablations attribute a 9.0% RMSE reduction to the R-GAT encoder alone. A group-conditional SHAP analysis further shows that graph-derived proximity to regional technology clusters is an unusually strong predictor for first-generation minority students, a signal that tabular features cannot recover and one we read as a within-model association rather than a policy lever.