标题： 选择要学习的内容：结合实验和观察证据时的实验设计 显示英文标题

标题： Choosing What to Learn: Experimental Design when Combining Experimental with Observational Evidence

摘要： 实验提供可信但通常是局部的效果，与特定的地点、人群或机制相关。 当这些估计不足以推广到更广泛的政策问题时，例如外部有效性及一般均衡（GE）效应，研究人员会将试验与来自简化形式或结构观察估计的外部证据，或先验实验相结合。 我们在这个背景下开发了一个统一的框架，用于设计实验：研究者从可行集合中选择要通过实验识别的参数（包括哪些处理组和/或个体参与实验），分配样本量，并指定如何加权实验和观察估计量。 由于观察输入可能以事先未知的方式存在偏差，我们开发了一个最小最大比例遗憾目标，该目标相对于一个知道偏差并同时选择设计和估计量的理想情况来评估任何候选设计。 这产生了一个透明的偏差-方差权衡，不需要预先指定的偏差界限，并且仅依赖于关于估计量精度和估计量对底层参数敏感性的信息。 我们通过（i）设计小规模现金转移实验以估计一般均衡效应，以及（ii）优化微金融干预的站点选择来说明该框架。 显示英文摘要

摘要： Experiments deliver credible but often localized effects, tied to specific sites, populations, or mechanisms. When such estimates are insufficient to extrapolate effects for broader policy questions, such as external validity and general-equilibrium (GE) effects, researchers combine trials with external evidence from reduced-form or structural observational estimates, or prior experiments. We develop a unified framework for designing experiments in this setting: the researcher selects which parameters to identify experimentally from a feasible set (which treatment arms and/or individuals to include in the experiment), allocates sample size, and specifies how to weight experimental and observational estimators. Because observational inputs may be biased in ways unknown ex ante, we develop a minimax proportional regret objective that evaluates any candidate design relative to an oracle that knows the bias and jointly chooses the design and estimator. This yields a transparent bias-variance trade-off that requires no prespecified bias bound and depends only on information about the precision of the estimators and the estimand's sensitivity to the underlying parameters. We illustrate the framework by (i) designing small-scale cash transfer experiments aimed at estimating GE effects and (ii) optimizing site selection for microfinance interventions.