Before revision:  Recurrent neural networks (RNNs) are used to predict counterfactual time-series of treated unit outcomes using only the outcomes of control units as inputs. Unlike SCM, the proposed method does not rely on pre-intervention covariates , allows for nonconvex combinations of control units, can handle multiple treated units, and can share model parameters across time-steps.
After revision:  Recurrent neural networks (RNNs) are used to predict counterfactual time-series of treated unit outcomes using only the outcomes of control units as predictors. This approach is less susceptible to p-hacking because it does not require the researcher to choose predictors or pre-intervention covariates , allows for nonconvex combinations of control units, can handle multiple treated units, and can share model parameters across time-steps.