Before revision:  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. RNNs 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 .
After revision:  Unlike SCM, the proposed method does not rely on pre-intervention covariates , allows for nonconvex combinations of control units, and are specifically structured to exploit temporal dependencies in sequential data. I apply the approach to the problem of estimating the long-run impacts of U.S. homestead policy on public school spending .