Estimating Survey Validity in Census Microdata Labs from Synthetic Data Generation

Abstract

The proliferation of high-dimensional census data has precipitated a growing tension between the imperative to democratize data access and the stringent legal requirements surrounding individual privacy. Microdata labs, traditionally the secure environments for empirical demographic research, increasingly rely on synthetic data generation to mitigate disclosure risks while attempting to preserve the analytical utility of the underlying datasets. However, evaluating whether synthetic data can reliably substitute original survey data for complex downstream sociological and economic modeling remains a profound methodological challenge. Traditional utility metrics, which predominantly focus on univariate or bivariate distributional equivalence, often fail to capture the deep, multipoint structural dependencies inherent in human population data. This paper proposes a novel framework for predicting the survey validity of synthetic census data by employing advanced graph analysis techniques. By transforming tabular microdata into complex network representations, we evaluate the topological fidelity of the synthetic data against its original counterpart. We explore how structural metrics, including centrality distributions, community modularity, and path-length preservation, serve as robust predictors for the validity of downstream regression and classification tasks. Through extensive simulated environments modeling national statistical office operations, our findings indicate that graph-based structural parity strongly correlates with high survey validity, offering a superior evaluation mechanism compared to traditional marginal preservation methods. The insights derived from this study provide a foundation for optimizing synthetic data generation pipelines in secure research facilities.