Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration

This paper presents a method for generating differentially private synthetic data that preserves statistical properties needed for causal inference, using maximum-entropy calibration techniques. The approach balances privacy protection with analytical utility, addressing a key challenge in regulated industries where sensitive data must be shared for research while maintaining individual privacy guarantees.

This paper presents a method for generating differentially private synthetic data that preserves statistical properties needed for causal inference, using maximum-entropy calibration techniques. The approach balances privacy protection with analytical utility, addressing a key challenge in regulated industries where sensitive data must be shared for research while maintaining individual privacy guarantees.

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