2017/12/12 by Gillian M. Raab, Gillian Raab, Beata Nowok +4
Decision Sciences · Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Applications (stat.AP) #FOS: Computer and information sciences #Health disparities and outcomes #demographic modeling and climate adaptation #stat.AP
paper · pdf · doi:10.48550/arxiv.1712.04078
arxiv created 2017/12/12 · openalex publication_date 2017/12/12 · arxiv updated 2017/12/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We report on our experiences of helping staff of the Scottish Longitudinal Study to create synthetic extracts that can be released to users. In particular, we focus on how the synthesis process can be tailored to produce synthetic extracts that will provide users with similar results to those that would be obtained from the original data. We make recommendations for synthesis methods and illustrate how the staff creating synthetic extracts can evaluate their utility at the time they are being produced. We discuss measures of utility for synthetic data and show that one tabular utility measure is exactly equivalent to a measure calculated from a propensity score. The methods are illustrated by using the R package synthpop to create synthetic versions of data from the 1901 Census of Scotland.