Early Academic Capital as the Causal Origin of Dropout in Constrained Educational Systems -- Evidence from Longitudinal Data and Structural Causal Models
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arXiv
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| Format: | Preprint |
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2026
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| author | Paz, Hugo Roger |
| author_facet | Paz, Hugo Roger |
| contents | 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 (passing at most 1 subject 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22772 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Early Academic Capital as the Causal Origin of Dropout in Constrained Educational Systems -- Evidence from Longitudinal Data and Structural Causal Models Paz, Hugo Roger Computers and Society 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 (passing at most 1 subject 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. |
| title | Early Academic Capital as the Causal Origin of Dropout in Constrained Educational Systems -- Evidence from Longitudinal Data and Structural Causal Models |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2604.22772 |