The Causal Effect of First-Time Academic Failure on University Dropout: Evidence from a Regression Discontinuity Design

• URL: http://arxiv.org/abs/2601.05987v1
• Date: Fri, 09 Jan 2026 18:08:15 GMT
• Title: The Causal Effect of First-Time Academic Failure on University Dropout: Evidence from a Regression Discontinuity Design
• Authors: H. R. Paz,
• Abstract summary: This study estimates the causal effect of first-time academic failure on subsequent university attrition.<n> Contrary to conventional assumptions, the results indicate that marginal first-time failure is associated with a lower probability of subsequent dropout.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: University dropout remains a persistent challenge in higher education systems, yet causal evidence on the mechanisms triggering early disengagement is limited. This study estimates the causal effect of first-time academic failure on subsequent university attrition. Exploiting a sharp institutional grading threshold on a 0-10 scale, we implement a regression discontinuity design (RDD) comparing students who narrowly fail to those who narrowly pass their first attempt. Using longitudinal administrative data spanning multiple cohorts and degree programmes, we estimate local average treatment effects (LATE) for students at the margin of success and examine dropout outcomes within 12 and 24 months following the initial evaluation. Contrary to conventional assumptions, the results indicate that marginal first-time failure is associated with a lower probability of subsequent dropout relative to marginal passing at both horizons. A comprehensive battery of robustness checks - including donut RDD specifications, placebo cutoffs, and formal density tests - supports the validity of the identification strategy. These findings suggest that early academic failure may function as a salient signal that prompts behavioural adjustment or reorientation, while marginal passing may sustain a state of "fragile persistence". The study provides causal evidence on the non-linear effects of early academic performance and highlights the importance of carefully designed institutional responses at critical evaluation thresholds.

Related papers

• Observationally Informed Adaptive Causal Experimental Design [55.998153710215654]
  We propose Active Residual Learning, a new paradigm that leverages the observational model as a foundational prior.<n>This approach shifts the experimental focus from learning target causal quantities from scratch to efficiently estimating the residuals required to correct observational bias.<n> Experiments on synthetic and semi-synthetic benchmarks demonstrate that R-Design significantly outperforms baselines.
  arXiv  Detail & Related papers  (2026-03-04T06:52:37Z)
• Effect-Level Validation for Causal Discovery [1.8192444294441061]
  Causal discovery is increasingly applied to large-scale telemetry data to estimate the effects of user-facing interventions.<n>But its reliability for decision-making in feedback-driven systems with strong self-selection remains unclear.<n>We propose an effect-centric, admissibility-first framework that treats discovered graphs as structural hypotheses.
  arXiv  Detail & Related papers  (2026-02-09T07:26:55Z)
• Early Warning Signals Appear Long Before Dropping Out: An Idiographic Approach Grounded in Complex Dynamic Systems Theory [1.732435844754418]
  When resilience weakens, students are at risk of disengagement and may drop out.<n>In this article, we test whether early warning signals of resilience loss can forecast disengagement before dropping out.
  arXiv  Detail & Related papers  (2026-01-16T18:43:46Z)
• Stabilising Learner Trajectories: A Doubly Robust Evaluation of AI-Guided Student Support using Activity Theory [1.2234742322758418]
  This study evaluates an AI-guided student support system at a large university using doubly robust score matching.<n>Results indicate that the intervention effectively stabilised precarious trajectories.<n>However, effects on the speed of qualification completion were positive but statistically constrained.
  arXiv  Detail & Related papers  (2025-12-11T22:28:12Z)
• The Stagnant Persistence Paradox: Survival Analysis and Temporal Efficiency in Exact Sciences and Engineering Education [0.0]
  This study applies a dual-outcome survival analysis framework to two key outcomes: definitive dropout and first major switch.<n>Results uncover a critical systemic inefficiency: a global median survival time of 4.33 years prior to definitive dropout, with a pronounced long tail of extended enrolment.<n>We argue that academic failure in rigid engineering curricula is not a sudden outcome but a long-tail process that generates high opportunity costs.
  arXiv  Detail & Related papers  (2025-12-04T14:11:28Z)
• Lost at the Beginning of Reasoning [85.17612793300238]
  We show that the first reasoning step exerts a disproportionately large influence on the final prediction.<n>We propose an efficient sampling strategy that leverages a reward model to identify and retain high-quality first reasoning steps.
  arXiv  Detail & Related papers  (2025-06-27T09:53:57Z)
• Data Fusion for Partial Identification of Causal Effects [62.56890808004615]
  We propose a novel partial identification framework that enables researchers to answer key questions.<n>Is the causal effect positive or negative? and How severe must assumption violations be to overturn this conclusion?<n>We apply our framework to the Project STAR study, which investigates the effect of classroom size on students' third-grade standardized test performance.
  arXiv  Detail & Related papers  (2025-05-30T07:13:01Z)
• Long-term Causal Inference via Modeling Sequential Latent Confounding [79.18609016557]
  Long-term causal inference is an important but challenging problem across various scientific domains.<n>We propose an approach based on the Conditional Additive Equi-Confounding Bias (CAECB) assumption.<n>Our proposed assumption states a functional relationship between sequential confounding biases across temporal short-term outcomes.
  arXiv  Detail & Related papers  (2025-02-26T09:56:56Z)
• Prediction-Powered Causal Inferences [59.98498488132307]
  We focus on Prediction-Powered Causal Inferences (PPCI)<n>We first show that conditional calibration guarantees valid PPCI at population level.<n>We then introduce a sufficient representation constraint transferring validity across experiments.
  arXiv  Detail & Related papers  (2025-02-10T10:52:17Z)
• Bayesian Causal Forests for Longitudinal Data: Assessing the Impact of Part-Time Work on Growth in High School Mathematics Achievement [0.0]
  We introduce a longitudinal extension of Bayesian Causal Forests. This model allows for the flexible identification of both individual growth in mathematical ability and the effects of participation in part-time work. Results reveal the negative impact of part time work for most students, but hint at potential benefits for those students with an initially low sense of school belonging.
  arXiv  Detail & Related papers  (2024-07-16T17:18:33Z)
• Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbations [9.463133630647569]
  We introduce Finite Rank-Biased Overlap (FRBO), an enhanced similarity tailored explicitly for finite rankings. We empirically investigate the impact of removing items at different positions within a temporally ordered sequence. Our results demonstrate that removing items at the end of the sequence has a statistically significant impact on performance.
  arXiv  Detail & Related papers  (2023-07-24T23:26:46Z)
• Difficult Lessons on Social Prediction from Wisconsin Public Schools [32.90759447739759]
  Early warning systems assist in targeting interventions to individual students by predicting which students are at risk of dropping out. Despite significant investments in their widespread adoption, there remain large gaps in our understanding of the efficacy of EWS. We present empirical evidence that the prediction system accurately sorts students by their dropout risk. We find that it may have caused a single-digit percentage increase in graduation rates, though our empirical analyses cannot reliably rule out that there has been no positive treatment effect.
  arXiv  Detail & Related papers  (2023-04-13T00:59:12Z)
• Long-term Causal Inference Under Persistent Confounding via Data Combination [38.026740610259225]
  We study the identification and estimation of long-term treatment effects when both experimental and observational data are available. Since the long-term outcome is observed only after a long delay, it is not measured in the experimental data, but only recorded in the observational data.
  arXiv  Detail & Related papers  (2022-02-15T07:44:20Z)
• Correct block-design experiments mitigate temporal correlation bias in EEG classification [68.85562949901077]
  We show that the main claim in [1] is drastically overstated and their other analyses are seriously flawed by wrong methodological choices. We investigate the influence of EEG temporal correlation on classification accuracy by testing the same models in two additional experimental settings.
  arXiv  Detail & Related papers  (2020-11-25T22:25:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.