Synthetic Data Generation for 

Survey Data

Discussion

Jingchen (Monika) Hu

Vassar College

FCSM 2023
Talk #1: Fully synthetic data for complex surveys

- Two proposals for generating fully synthetic data for complex surveys: 

Synrep-R and Synrep-1

- Both proposals: 1) resample via Bayesian bootstrap (M samples), 2) use the 

WFPBB to make pseudo-populations, and 3) draw SRS from each 

pseudo-population

- These two proposals differ in how to generate synthetic data replicates

- Synrep-R: generating multiple (R) synthetic data sets from each SRS; total R*M

- Synrep-1: generating one synthetic data set from each SRS: total M

- Newly developed combining rules for both proposals to enable variance 

estimation

- Questions: 

- Advice for values of M and R in practice?

- Can popular synthesizers such as CART be easily implemented within these two proposals? 

Need for more synthesis models development?

- What to do if the goal is to release fully synthetic populations?
Talk #2: Synthetic population generation for nested data 

using differentially private posteriors

- The work is built on the Pseudo Posterior Mechanism for DP synthetic data

- Record-indexed privacy weights

- Allowing surgical downweighting of high-risk records

- Asymptotic DP

- Current work is extending 

- The neighborhood concept: from one record to one group of records

- To latent variables: challenge of working with integrated likelihood

- The weighting approach: challenge of two sets of weights (individual- and group-levels)

- Preliminary simulation results show little gain in privacy or utility for additional 

downweighting of groups after downweighting of individuals

- Questions:

- Any intuition of why there is little gain in privacy or utility shown in the preliminary results?

- Any intuition for when group sizes differ?
Talk #3: Private table statistics using synthetic microdata 

generation

- The work is built on the Pseudo Posterior Mechanism for DP synthetic data

- Current work is incorporating survey weights by modeling and use table 

statistics as data utility metrics

- Two fully synthesizing models: FBS for sample and FBP for population

- SDR application shows better performance of FBS in terms of count table and 

better performance of FBP in terms of average salary table

- Simulation studies with (more) informative sampling design show even better 

performance of FBS and FBP as Laplace Mechanism destroys utility

- Questions:

- How challenging is it to extend the FBS and FBP for multivariate outcome variables?

- How to effectively choose m to balance the privacy-utility tradeoff?
Talk #4: Calibration procedure for estimates obtained from 

posterior approximation algorithms, with application to 

domain-level modeling

- Goal is to develop a practical tool for the evaluation of the fit and correction of 

a bias in the posterior variance of the ADVI-based point estimator

- Proposal of a resampling-based approach

- Assumption: model is correct but the fitting algorithm may produce systematic errors in 

uncertainty measures

- Goal: to evaluate and potentially correct the fit

- Method: adjustment of the pivot with pivot-based CIs and re-scaled CIs

- Simulation and CES results are positive

- Questions:

- Are you considering extension of the method when the correct model assumption does not 

hold?

- Can the proposed methods extend beyond the FH and CCFH models?
Thank you! Questions?

Jingchen (Monika) Hu

jihu@vassar.edu

Vassar College