["Journal of Applied Econometrics, EarlyView. ", "\nABSTRACT\nWhile existing work shows COVID‐19 stay‐at‐home (SAH) policies decreased mobility on average, we lack evidence regarding heterogeneity in policy effectiveness across US counties. To uncover potential heterogeneity, we implement a novel two‐stage approach. First, we employ recent advances in synthetic control estimation and inference to obtain and bound both time‐average and county‐specific treatment effects. Next, we use a local linear forest to explain observed heterogeneity using county‐level predictors. This procedure reveals substantial variation in policy effectiveness and identifies predictors of policy effectiveness which can be used to target future SAH policies.\n"]