We recast the synthetic controls for evaluating policies as a counterfactual prediction problem and replace its linear regression with a non-parametric 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 movement 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 movement was announced on December 6, 2017. Using conformal inference tests, we justify our model and find the increase to be statistically significant. \\%DIF > over 48 weeks since the relocation was announced on December 6, 2017. By using conformal inference and placebo tests, we justify our model and find the increase to be statistically significant.
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 accurate counterfactual predictions and we provide theoretical guarantees . We apply our method to a highly debated policy: the relocation of the US embassy to Jerusalem. In Israel and Palestine, we find that the average number of weekly conflicts has increased by roughly 103 \\%DIF > over 48 weeks since the relocation was announced on December 6, 2017. By using conformal inference and placebo tests, we justify our model and find the increase to be statistically significant.
R
non-parametric
nonparametric
D
more
A
and we provide theoretical guarantees
R
highly-debated
highly debated
R
movement
relocation
D
\% over 48 weeks since the movement was announced on December 6, 2017. Using conformal inference tests, we justify our model and find the increase to be statistically significant.