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摘要翻译：
实验已经成为指导决策和政策选择的一种越来越普遍的工具。设计实验的一个常见障碍是缺乏统计能力。本文研究了在处理一旦实施不能轻易移除的约束条件下的最优多周期试验设计；例如，一国政府可能在不同的时间在不同的地区实施公共卫生干预，由于实际的限制，这种治疗不能很容易地取消。治疗设计问题是选择在哪个时间治疗哪个地区（以单位为单位），目的是检验关于治疗效果的假设。当潜在结果是单位效应和时间效应的线性函数，且观测/潜在协变量离散时，我们给出了最优治疗设计问题的解析可行解，其中治疗效果估计量的方差至多为最优治疗设计方差的1+O(1/N^2)倍，其中N是单位数。该解决方案以交错处理采用模式分配单元--如果处理只影响一个周期，则每个周期中被处理单元的最佳比例随时间线性增加；如果处理影响多个时期，最优分数在时间上呈非线性增加，开始时较小，结束时较大。在结果依赖于潜在协变量的一般情况下，我们证明历史数据可以用于设计实验。我们提出了一种数据驱动的局部搜索算法来为治疗时间分配单位。通过对流感发病率的综合干预和对家庭医疗服务和食品支出干预的综合实验，我们证明了我们的方法改进了基准实验设计。
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英文标题：
《Optimal Experimental Design for Staggered Rollouts》
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作者：
Ruoxuan Xiong, Susan Athey, Mohsen Bayati, Guido Imbens
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最新提交年份：
2020
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
Experimentation has become an increasingly prevalent tool for guiding decision-making and policy choices. A common hurdle in designing experiments is the lack of statistical power. In this paper, we study the optimal multi-period experimental design under the constraint that the treatment cannot be easily removed once implemented; for example, a government might implement a public health intervention in different geographies at different times, where the treatment cannot be easily removed due to practical constraints. The treatment design problem is to select which geographies (referred by units) to treat at which time, intending to test hypotheses about the effect of the treatment. When the potential outcome is a linear function of unit and time effects, and discrete observed/latent covariates, we provide an analytically feasible solution to the optimal treatment design problem where the variance of the treatment effect estimator is at most 1+O(1/N^2) times the variance using the optimal treatment design, where N is the number of units. This solution assigns units in a staggered treatment adoption pattern - if the treatment only affects one period, the optimal fraction of treated units in each period increases linearly in time; if the treatment affects multiple periods, the optimal fraction increases non-linearly in time, smaller at the beginning and larger at the end. In the general setting where outcomes depend on latent covariates, we show that historical data can be utilized in designing experiments. We propose a data-driven local search algorithm to assign units to treatment times. We demonstrate that our approach improves upon benchmark experimental designs via synthetic interventions on the influenza occurrence rate and synthetic experiments on interventions for in-home medical services and grocery expenditure.
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PDF链接：
https://arxiv.org/pdf/1911.03764
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