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News/Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration
arxiv
PublishedJuly 10, 2026 at 4:00 AM
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Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration

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Publisher summary· verbatim

arXiv:2607.08122v1 Announce Type: new Abstract: 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

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#synthetic-data#differential-privacy#causal-inference

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