论文标题
推断使用高斯过程的时间对干预措施的因果影响
Inference on Causal Effects of Interventions in Time using Gaussian Processes
论文作者
论文摘要
本文着重于将干预措施在特定时间点的因果影响进行推断，这在结果变量随时间变化中所表现出来。我们通过中断的时间序列框架运行，并扩展了诸如合成控制（Abadie 2003）和贝叶斯结构时间序列（Brodersen等人，2015年）之类的方法，通过基于高斯流程的非参数配方替换了基本的动态线性回归模型。开发的模型具有高度的灵活性，对功能形式的局限性很小，并允许在贝叶斯框架下通过其估计来纳入不确定性。我们介绍了两个非参数结构时间序列模型的家族，要么仅在结果变量的轨迹上运行，要么使用多个输出高斯过程在多元设置的轨迹上运行。该论文与案例研究紧密相关，该案例研究着重于与欧洲其他地区形成鲜明对比的加速英国疫苗接种计划的影响，以说明方法论并介绍实施程序。
This paper focuses on drawing inference on the causal impact of an intervention at a specific time point, as manifested in an outcome variable over time. We operate on the interrupted time series framework and expand on approaches such as the synthetic control (Abadie 2003) and Bayesian structural time series (Brodersen et al 2015), by replacing the underlying dynamic linear regression model with a non-parametric formulation based on Gaussian Processes. The developed models possess a high degree of flexibility posing very little limitations on the functional form and allow to incorporate uncertainty, stemming from its estimation, under the Bayesian framework. We introduce two families of non-parametric structural time series models either operating on the trajectory of the outcome variable alone, or in a multivariate setting using multiple output Gaussian processes. The paper engages closely with a case study focusing on the impact of the accelerated UK vaccination schedule, as contrasted with the rest of Europe, to illustrate the methodology and present the implementation procedure.
