Towards Differentially Private Causal Inference Using Synthetic Controls

Music City Center 

Synthetic control is a widely used causal inference method for evaluating the effectiveness of government policies, such as new tariffs and tax increases. Traditionally, synthetic control is applied to aggregate-level datasets, but more recent studies have explored its applications to disaggregated datasets, such as individual health records and targeted marketing analyses. However, individual-level datasets exhibit different distributional properties compared to aggregate-level data. Our work reexamines the implicit assumptions of traditional synthetic control approaches and proposes theoretically grounded algorithms for synthetic control in individual-level analyses. I will address privacy concerns related to individual-level data analyses and present our work on Differentially Private Synthetic Control (DPSC). I will also discuss Cluster Synthetic Control, a synthetic control approach that incorporates a donor selection step, which eventually helps DPSC. These methods provide synthetic control approaches with provable accuracy improvements and privacy guarantees.