Dropout in higher education is commonly analysed through observable academic events such as course failure or repetition. However, these event-based perspectives may obscure the underlying structural dynamics that shape student trajectories. In this study, we adopt a causal computational social science approach to identify the origins of dropout in a constrained engineering curriculum. Using longitudinal administrative data from 16,868 students who survived to their second active term, and a leakage-free panel design, we estimate the causal effect of early academic capital accumulation on three-year dropout. Treatment is defined as low early progress (≤ 1 subject passed by the end of the second term). We employ G-estimation of structural nested mean models, complemented by marginal structural models with inverse probability weighting. We find a large and robust causal effect: low early academic capital increases dropout probability by 25.3 percentage points (G-estimation), closely matched by a 27.4 pp estimate from IPTW models. This effect is approximately twice as large as the estimated direct impact of later academic events such as first-time gateway-course repetition (12.7 pp). These findings suggest that dropout does not originate in isolated academic failures, but in early trajectory misalignment between academic progress and system-imposed temporal constraints. This perspective shifts the focus of intervention from downstream events to early-stage trajectory formation.
Hugo Roger Paz (Thu,) studied this question.