  Conducting causal inference with panel data is a core challenge in social
science research. We adapt a deep neural architecture for time series
forecasting (the N-BEATS algorithm) to more accurately impute the
counterfactual evolution of a treated unit had treatment not occurred. Across a
range of settings, the resulting estimator (``SyNBEATS'') significantly
outperforms commonly employed methods (synthetic controls, two-way fixed
effects), and attains comparable or more accurate performance compared to
recently proposed methods (synthetic difference-in-differences, matrix
completion). An implementation of this estimator is available for public use.
Our results highlight how advances in the forecasting literature can be
harnessed to improve causal inference in panel data settings.
