Abstract
When evaluating the impact of a policy (e.g., gun control) on a metric of interest (e.g., crime-rate), it may not be possible or feasible to conduct a randomized control trial. In such settings where only observational data is available, synthetic control (SC) methods [1-3] provide a popular data-driven approach to estimate a "synthetic" or "virtual" control by combining measurements of "similar" alternatives or units (called "donors"). Recently, robust synthetic control (RSC) [4] was proposed as a generalization of SC to overcome the challenges of missing data and high levels of noise, while removing the reliance on expert domain knowledge for selecting donors. However, both SC and RSC (and its variants) suffer from poor estimation when the pre-intervention period is too short.
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