Optimized Regression Discontinuity Designs
Optimized Regression Discontinuity Designs
复制标题
DOI：
10.1162/rest_a_00793
复制
发表时间：
2019-05-01
影响因子：
8
通讯作者：
Wager, Stefan
中科院分区：
文献类型：
--
作者：
Imbens, Guido;Wager, Stefan
在观察性研究中，用于因果推断的回归不连续方法越来越受欢迎，导致了不同估计策略的扩散，其中大多数涉及首先在治疗分配边界的两侧拟合非参数回归模型，然后报告感兴趣的效应的插件估计。然而，在应用中，通常很难以针对特定推断目标进行良好校准的方式调整非参数回归;例如，具有最佳全局样本内拟合的模型可能提供对不连续性参数的较差估计，这取决于边界点处的回归函数。我们提出了一种替代方法，估计和统计推断回归不连续性设计，使用数值凸优化直接获得有限样本极大极小线性估计的回归不连续性参数，受限制的条件响应函数的二阶导数。给定二阶导数的界，我们所提出的方法是完全数据驱动的，并提供离散和连续运行变量的回归不连续参数的统一置信区间。该方法也自然地扩展到多个运行变量的情况。
The increasing popularity of regression discontinuity methods for causal inference in observational studies has led to a proliferation of different estimating strategies, most of which involve first fitting nonparametric regression models on both sides of a treatment assignment boundary and then reporting plug-in estimates for the effect of interest. In applications, however, it is often difficult to tune the nonparametric regressions in a way that is well calibrated for the specific target of inference; for example, the model with the best global in-sample fit may provide poor estimates of the discontinuity parameter, which depends on the regression function at boundary points. We propose an alternative method for estimation and statistical inference in regression discontinuity designs that uses numerical convex optimization to directly obtain the finite-sample-minimax linear estimator for the regression discontinuity parameter, subject to bounds on the second derivative of the conditional response function. Given a bound on the second derivative, our proposed method is fully data driven and provides uniform confidence intervals for the regression discontinuity parameter with both discrete and continuous running variables. The method also naturally extends to the case of multiple running variables.