Before revision:  Encoder-decoder networks, which are special class of RNNs suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes . The proposed method does not rely on pretreatment covariates and encoder-decoder networks are capable of learning nonconvex combinations of control unit outcomes to construct a counterfactual.
After revision:  Encoder-decoder networks, which are special class of RNNs suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes using only the pre-intervention outcomes of control units as inputs. Unlike the synthetic control method, the proposed method does not rely on pretreatment covariates and encoder-decoder networks are capable of learning nonconvex combinations of control unit outcomes to construct a counterfactual.