Skip to yearly menu bar Skip to main content


Differentially Private Synthetic Control

Saeyoung Rho · Rachel Cummings · Vishal Misra

Auditorium 1 Foyer 155


Synthetic control is a causal inference tool used to estimate the treatment effects of an intervention by creating synthetic counterfactual data. This approach combines measurements from other similar observations (i.e., donor pool) to predict a counterfactual time series of interest (i.e., target unit) by analyzing the relationship between the target and the donor pool before the intervention. As synthetic control tools are increasingly applied to sensitive or proprietary data, formal privacy protections are often required. In this work, we provide the first algorithms for differentially private synthetic control with explicit error bounds. Our approach builds upon tools from non-private synthetic control and differentially private empirical risk minimization. We provide upper and lower bounds on the sensitivity of the synthetic control query, explicit error bounds on the accuracy of our private synthetic control algorithms, and experimental results on synthetic data.

Live content is unavailable. Log in and register to view live content