Robust Synthetic Control

Understand

We present a robust generalization of the synthetic control method for comparative case studies.

• Like the classical method, we present an algorithm to estimate the unobservable counterfactual of a treatment unit.
• A distinguishing feature of our algorithm is that of de-noising the data matrix via singular value thresholding, which renders our approach robust in multiple facets: it automatically identifies a good subset of donors, overcomes the challenges of missing data, and continues to work well in settings where covariate information may not be provided.
• To begin, we establish the condition under which the fundamental assumption in synthetic control-like approaches holds, i.e.