论文标题
回归不连续设计中噪声引起的随机化
Noise-Induced Randomization in Regression Discontinuity Designs
论文作者
论文摘要
回归不连续性设计评估在设置中通过观察到的可变量确定治疗的因果效应。在这里，我们提出了一种在运行变量中使用有关外源噪声（例如测量误差）知识的回归不连续设计的识别，估计和推断的新方法。在我们的策略中，我们对处理和控制单位进行了加权，以平衡一个潜在变量，该变量的运行变量是嘈杂的度量。我们的方法是由运行变量中的噪声提供的有效随机化驱动的，并补充了对连续性论点的吸引人的标准正式分析，同时忽略了分配机制的随机性。
Regression discontinuity designs assess causal effects in settings where treatment is determined by whether an observed running variable crosses a pre-specified threshold. Here we propose a new approach to identification, estimation, and inference in regression discontinuity designs that uses knowledge about exogenous noise (e.g., measurement error) in the running variable. In our strategy, we weight treated and control units to balance a latent variable of which the running variable is a noisy measure. Our approach is driven by effective randomization provided by the noise in the running variable, and complements standard formal analyses that appeal to continuity arguments while ignoring the stochastic nature of the assignment mechanism.
