论文标题
预测与面板数据的因果推断的算法
Forecasting Algorithms for Causal Inference with Panel Data
论文作者
论文摘要
使用面板数据进行因果推断是社会科学研究的核心挑战。我们适应时间序列预测（N-Beats算法）的深度神经结构，以更准确地算上处理过的单元的反事实演变，但没有发生治疗。在一系列设置中，所得的估计器（````合成'''显着胜过通常使用的方法（合成控制，双向固定效应），与最近提出的方法相比，具有可比或更准确的性能（合成差异差异差异，矩阵完成，矩阵完成）。该估算器的实现可供公开使用。我们的结果突出了如何利用预测文献的进步来改善面板数据设置的因果推断。
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.
