Regression Adjustments for Estimating the Global Treatment Effect in Experiments with Interference
Regression Adjustments for Estimating the Global Treatment Effect in Experiments with Interference
复制标题
DOI：
10.1515/jci-2018-0026
复制
发表时间：
2019-09-01
影响因子：
1.4
通讯作者：
Chin, Alex
中科院分区：
文献类型：
--
作者：
Chin, Alex
在存在干扰的情况下，全球平均治疗效果的标准估计可能会出现偏差。本文提出了回归调整估计量，用于消除伯努利随机实验中干扰造成的偏差。我们使用拟合模型来预测全局控制和全局治疗的反事实结果。我们的工作与标准回归调整的不同之处在于，调整变量是根据治疗分配向量的函数构建的，并且我们允许研究人员使用与响应相关的任何函数的集合，将检测干扰的问题转变为特征工程问题。我们在线性模型设置中描述了所提出的估计量的分布，并将结果与 SUTVA 下的回归调整标准理论联系起来。然后，我们提出了一种估计器，允许使用灵活的机器学习估计器来拟合非线性干扰函数形式。我们建议通过引导和重采样方法进行统计推断，这使我们能够避开干扰所暗示的复杂依赖性，而是依赖于经验协方差结构。这种方差估计依赖于类似于观察研究中引用的标准无混杂性假设的外生性假设。在模拟实验中，我们的方法比现有的基于邻域暴露模型的逆倾向加权估计器更能更好地消除估计值的偏差。我们使用我们的方法重新分析了在中国农村的一些村庄进行的有关天气保险采用的实验。
Standard estimators of the global average treatment effect can be biased in the presence of interference. This paper proposes regression adjustment estimators for removing bias due to interference in Bernoulli randomized experiments. We use a fitted model to predict the counterfactual outcomes of global control and global treatment. Our work differs from standard regression adjustments in that the adjustment variables are constructed from functions of the treatment assignment vector, and that we allow the researcher to use a collection of any functions correlated with the response, turning the problem of detecting interference into a feature engineering problem. We characterize the distribution of the proposed estimator in a linear model setting and connect the results to the standard theory of regression adjustments under SUTVA. We then propose an estimator that allows for flexible machine learning estimators to be used for fitting a nonlinear interference functional form. We propose conducting statistical inference via bootstrap and resampling methods, which allow us to sidestep the complicated dependences implied by interference and instead rely on empirical covariance structures. Such variance estimation relies on an exogeneity assumption akin to the standard unconfoundedness assumption invoked in observational studies. In simulation experiments, our methods are better at debiasing estimates than existing inverse propensity weighted estimators based on neighborhood exposure modeling. We use our method to reanalyze an experiment concerning weather insurance adoption conducted on a collection of villages in rural China.