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This paper introduces causal workloads, a novel approach to generating differentially private synthetic data that preserves crucial causal moments necessary for accurate causal inference, particularly for average treatment effects (ATE). By employing maximum-entropy calibration, the authors demonstrate that their method can effectively balance treatment-arm distributions while maintaining privacy, allowing for robust causal estimations without additional privacy costs. Empirical results reveal that while generic workloads excel in point accuracy under relaxed privacy constraints, causal workloads significantly enhance the reliability of causal estimates at strict privacy budgets.
Causal workloads can unlock accurate causal inference from differentially private synthetic data without incurring extra privacy costs, revealing a critical tradeoff between distributional fidelity and valid causal estimation.
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 sampling, privacy, workload-approximation, Monte Carlo, and calibration terms. We also introduce Causal-AIM, an adaptive workload selector, and a noise-aware multiple-imputation (NA+MI) procedure for confidence intervals from DP synthetic data. Because the workload is released once, the same DP synthetic table can support ATE, ATT, and subgroup analyses without additional privacy spending. Empirically, causal workloads are most useful at strict privacy budgets and for calibrated uncertainty, while generic workloads often retain an advantage for point RMSE as privacy relaxes. The broader lesson is a tradeoff: distributional fidelity can help point accuracy, but valid causal inference requires preserving causal moments and propagating DP noise rather than treating synthetic rows as real.
