社會科學領域中，單條件變量的間斷回歸估計方法已被廣泛運用。由單條件變量決定的機制確實廣泛，但許多實證研究更顯示了多條件變量設計的普遍性。再者，文獻在過去著重平均政策效果的估計，而忽略了估計分位數效果可提供的政策資訊。本文針對多條件變量的間斷回歸，提出新的估計方法。此無母數方法可運用於平均效果與分位數效果的估計。根據蒙地卡羅模擬分析，過去方法的表現皆嚴重受到交乘項的納入與條件變量的波動影響。本文提出的估計方法對於所有模擬情況都表現穩定，且其估計結果亦較現有的方法準確。

The estimation methods of regression discontinuity (RD) designs with a single assignment variable have recently been acknowledged to have a wide range of applications in social science. While treatment assignment is often determined by one threshold value, many empirical studies have shown the pervasiveness of RD designs with more than one assignment variable. Moreover, the literature has focused on the average treatment effect and overlooked the interesting perspectives provided by treatment effects at different quantiles of the outcome distribution. In this paper, we propose new approaches for RD designs with multiple assignment variables. The approaches allow nonparametric estimation and could be applied to estimating average treatment effects and quantile treatment effects. Based on our Monte Carlo simulation study, we suggest that the performance of the existing approaches is sensitive to the interaction terms in data generating processes as well as large variations in assignment variables. Our new approaches produce robust and more accurate estimates compared to the existing approaches with respect to all scenarios.

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