标题： 基于回归和设计的时序实验因果推断统一方法 显示英文标题

标题： Unifying regression-based and design-based causal inference in time-series experiments

摘要： 时间序列实验，也称为切换实验或N-1试验，在现代医疗和工业应用中发挥着越来越重要的作用。 在潜在结果框架下，最近的研究从设计的角度研究了时间序列实验，仅依赖于设计中的随机性来进行统计推断。 专注于更简单的统计方法，我们考察了基于回归的方法在时间序列实验中估计处理效应的设计基础性质。 我们证明，使用适当指定的工作模型和转换的回归变量，可以一致地估计感兴趣的处理效应。 我们的分析允许同时估计发散数量的处理效应，并建立了基于回归的估计量的一致性和渐近正态性。 此外，我们表明，从渐近角度看，异方差和自相关一致的方差估计量提供了对真实设计基础方差的保守估计。 重要的是，尽管我们的方法依赖于回归，但我们的设计基础框架允许回归模型的误设。 显示英文摘要

摘要： Time-series experiments, also called switchback experiments or N-of-1 trials, play increasingly important roles in modern applications in medical and industrial areas. Under the potential outcomes framework, recent research has studied time-series experiments from the design-based perspective, relying solely on the randomness in the design to drive the statistical inference. Focusing on simpler statistical methods, we examine the design-based properties of regression-based methods for estimating treatment effects in time-series experiments. We demonstrate that the treatment effects of interest can be consistently estimated using ordinary least squares with an appropriately specified working model and transformed regressors. Our analysis allows for estimating a diverging number of treatment effects simultaneously, and establishes the consistency and asymptotic normality of the regression-based estimators. Additionally, we show that asymptotically, the heteroskedasticity and autocorrelation consistent variance estimators provide conservative estimates of the true, design-based variances. Importantly, although our approach relies on regression, our design-based framework allows for misspecification of the regression model.