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 predict the counterfactual evolution of a treated unit had treatment not occurred. The resulting estimator (“SyNBEATS”) significantly outperforms two-way fixed effect, synthetic control, and matrix completion methods across a range of settings. In addition, it attains comparable or more accurate performance relative to a recently proposed method, synthetic difference-in-differences. Our results highlight how advances in the forecasting literature can be harnessed to improve causal inference in panel settings.