This paper proposes an alternative to the synthetic control method (SCM) for estimating the effect of a policy intervention on an outcome over time. Encoder-decoder 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, and can handle multiple treated units . 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 .
This paper proposes an alternative to the synthetic control method (SCM) for estimating the effect of a policy intervention on an outcome over time. 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. 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 .
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predictive accuracy while using much less information to produce counterfactual predictions
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