Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration
Workload-based differentially private (DP) synthetic data methods privately measure aggregate queries and post-process the noisy answers into synthetic records. Generic workloads can achieve strong distributional fidelity, but causal estimands such as the average treatment effect (ATE) depend on treatment-arm balance and outcome moments that generic marginals need not preserve. We propose causal workloads: DP query sets designed around the orthogonal moments used by doubly robust causal estimators. The released workload can be used directly by stable moment-map estimators or reconstructed by maximum-entropy calibration into reusable synthetic data; our theory decomposes ATE error into sam...
| Authors | Amir Asiaee, Kaveh Aryan |
|---|---|
| Primary category | cs.LG |
| Published | 9 July 2026 |
| Last updated at source | 17 July 2026 |
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