提出一种更稳健的因果效应估计方法，只需一个模型正确就能保证结果准确。
A Note on Doubly Robust Estimator in Regression Discontinuity Designs
- 结合两个不同模型的预测结果，提升估计可靠性
- 只要其中一个模型正确，估计结果就一致，无需两者都对
- 适合处理数据不完美或模型不确定的因果推断场景
本文提出一种用于回归不连续设计（RD）的双重稳健（DR）估计器。RD设计通过运行变量是否超过预设阈值来决定处理分配，是一种准实验方法。传统方法多采用局部线性回归等非参数估计，但其有效性依赖于非参数估计的一致性。本文提出的DR-RD估计器将两种不同的条件期望结果估计器结合起来，其核心优势在于：只要其中任一估计器一致，治疗效应估计即保持一致。该方法显著提升了RD设计中因果效应估计的稳健性。
原文摘要 · Abstract (English)
This note introduces a doubly robust (DR) estimator for regression discontinuity (RD) designs. RD designs provide a quasi-experimental framework for estimating treatment effects, where treatment assignment depends on whether a running variable surpasses a predefined cutoff. A common approach in RD estimation is the use of nonparametric regression methods, such as local linear regression. However, the validity of these methods still relies on the consistency of the nonparametric estimators. In this study, we propose the DR-RD estimator, which combines two distinct estimators for the conditional expected outcomes. The primary advantage of the DR-RD estimator lies in its ability to ensure the consistency of the treatment effect estimation as long as at least one of the two estimators is consistent. Consequently, our DR-RD estimator enhances robustness of treatment effect estimators in RD designs.
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