# An Auditable Policy-Simulation Framework for Student Dropout in Intervention-Free Data

arXiv: https://arxiv.org/abs/2604.08874 · HTML: https://mlsanity.com/paper/2604.08874/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 64 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 4 of 4)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks](https://mlsanity.com/paper/2003.01176/) (2020)
- [Prediction under interventions: evaluation of counterfactual performance using longitudinal observational data](https://mlsanity.com/paper/2304.10005/) (2023)
- [Estimating and evaluating counterfactual prediction models](https://mlsanity.com/paper/2308.13026/) (2023)

## Abstract (arXiv)

This study proposes a temporal modeling framework with a counterfactual policy-simulation layer for student dropout in higher education, using LMS engagement data and administrative withdrawal records. Dropout is operationalized as a time-to-event outcome at the enrollment level; weekly risk is modeled in discrete time via penalized, class-balanced logistic regression over person--period rows. Under a late-event temporal holdout, the model attains row-level AUCs of 0.8350 (train) and 0.8405 (test), with aggregate calibration acceptable but sparsely supported in the highest-risk bins. Ablation analyses indicate performance is sensitive to feature set composition, underscoring the role of temporal engagement signals. A scenario-indexed policy layer produces survival contrasts Delta S(T) under an explicit trigger/schedule contract: positive contrasts are confined to the shock branch (Trm policy=18: 0.0102, 0.0260, 0.0819), while the mechanism-aware branch is negative (Delta Srm mech(18)=-0.0078, Delta Srm mech(38)=-0.0134). A subgroup analysis by gender quantifies scenario-induced survival gaps via bootstrap; contrasts are directionally stable but small. Results are not causally identified; they demonstrate the framework's capacity for internal structural scenario comparison under observational data constraints.