Causal-Privacy Audit Workflow for Synthetic and Distilled Data in Dropout Support

2026-06-14

AI Summary

This work proposes CaP-Eval, a multidimensional auditing framework that jointly evaluates the causal fidelity, predictive utility, estimation robustness, and privacy risk of synthetic data while preserving key causal effects—such as the influence of socioeconomic status on intervention outcomes. Integrating time-aware adjustments, ensemble estimators, and empirical privacy screening, the framework systematically compares inverse probability weighting, double machine learning, DPGNet, and various synthetic data generation methods in an educational dropout intervention scenario. Experimental results demonstrate that DPGNet consistently preserves the direction and ranking of causal effects across privacy budgets, achieving minimal bias at ε=10. While distilled data exhibits high causal fidelity, it incurs substantial privacy risks from proximity to training records. Conventional synthetic methods generally attenuate effect magnitudes, compromising causal validity.

Abstract

Synthetic and distilled student data are increasingly used to enable privacy-conscious learning analytics, yet their suitability for decision-facing institutional support remains uncertain. In dropout support, generated data must preserve not only predictive utility or distributional resemblance, but also the financial-status evidence used to guide advising, payment-plan assistance, and scholarship-related decisions. Method: This study introduces CaP-Eval, a decision-facing causal-privacy audit workflow for evaluating generated student data under a fixed estimand, timing-aware adjustment design, estimator set, and empirical privacy-governance screen. The workflow compares original, distilled, adversarial synthetic, statistical synthetic, and DPGNet privacy-oriented generated data on predictive utility, treatment-effect fidelity, robustness to alternative estimators, and local training-record proximity. Results: DPGNet and distilled data preserved the original financial-status treatment-effect structure more reliably than the adversarial and Gaussian Copula baselines. DPGNet preserved full direction and rank agreement across epsilon levels; epsilon = 10 produced the smallest non-original IPW and DML deviations, while epsilon = 1 and epsilon = 5 amplified several financial-status contrasts. Distilled data remained highly faithful but retained the strongest local training-record proximity signal. TabularGNet preserved qualitative directions with moderate attenuation, and Gaussian Copula compressed effect magnitudes. Conclusions: Predictive utility, privacy orientation, empirical disclosure signals, and causal fidelity diverged; generated student data require joint audits of direction, magnitude, overlap, and release-governance risk before decision use.

Problem

• synthetic data
• student dropout support
• causal fidelity
• privacy-preserving data
• decision-facing analytics

Innovation

• causal-privacy audit
• synthetic data
• distilled data
• treatment-effect fidelity
• DPGNet