We recast the synthetic controls for evaluating policies as a counterfactual prediction problem and replace its linear regression with a nonparametric model inspired by machine learning. The proposed method enables us to achieve more accurate counterfactual predictions. We apply our method to a highly-debated policy: the move of the US embassy to Jerusalem. In Israel and Palestine, we find that the average number of weekly conflicts has increased by roughly 103% over 48 weeks since the move was announced on December 6, 2017. Using conformal inference and placebo tests, we justify our model and find the increase to be statistically significant.

我们将评估策略的综合控制重新定义为一个反事实的预测问题，并将其线性回归替换为由机器学习激发的非参数模型。该方法使我们能够获得更准确的反事实预测。我们将我们的方法应用于一项备受争议的政策:将美国大使馆迁往耶路撒冷。在以色列和巴勒斯坦，我们发现自2017年12月6日宣布撤军以来的48周内，每周冲突的平均数量增加了约103%。使用适形推理和安慰剂试验，我们证明了我们的模型，并发现增加具有统计学意义。