Before revision:  In empirical and simulated data applications, RNN-based models outperform SCM in terms of predictive accuracy while using much less information to produce counterfactual predictions .
After revision:  In empirical and simulated data applications, RNN-based models outperform SCM in terms of recovering experimental estimates from a field experiment extended to a time-series observational setting. In placebo tests run on three different benchmark datasets, RNNs are more accurate than SCM in predicting the post-intervention time-series of control units, while yielding a comparable proportion of false positives. The proposed method contributes to a new literature that uses machine learning techniques for data-driven counterfactual prediction .