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 (or moments) to identify experimentally from a feasible set (e.g., 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.

翻译：实验能够提供可信的效应估计，但这些效应通常具有局部性，局限于特定地点、人群或机制。当此类估计不足以外推至更广泛的政策问题（如外部有效性和一般均衡效应）时，研究者会将试验与来自简化形式或结构性观察估计的外部证据，或先前实验相结合。我们为此情境下的实验设计开发了一个统一框架：研究者从可行集合（例如，实验中应包含哪些处理组和/或个体）中选择要通过实验识别的参数（或矩），分配样本量，并确定如何加权实验估计量与观察估计量。由于观察性输入可能存在事前未知的偏差，我们开发了一个极小化极大比例遗憾目标函数，该函数通过对比一个知晓偏差并联合选择设计与估计量的预言机来评估任何候选设计。这产生了一个透明的偏差-方差权衡，无需预设偏差界限，且仅依赖于估计量精度信息以及估计目标对底层参数的敏感性。我们通过以下示例阐释该框架：（i）设计旨在估计一般均衡效应的小规模现金转移实验，以及（ii）优化小额信贷干预的站点选择。