Why Synthetic Control estimators are biased and what to do about it: Introducing Relaxed and Penalized Synthetic Controls
合成控制为何有偏及其修正：放松与惩罚合成控制
This paper extends the literature on the theoretical properties of synthetic controls to the case of non-linear generative models, showing that the synthetic control estimator is generally biased in such settings. I derive a lower bound for the bias, showing that the only component of it that is affected by the choice of synthetic control is the weighted sum of pairwise differences between the treated unit and the untreated units in the synthetic control. To address this…