ABSTRACT Causal inference and generalizability both matter. Historically, systematic designs emphasize causal inference, while representative designs focus on generalizability. Here, we suggest a transformative synthesis – Systematic Representative Design (SRD) – concurrently enhancing both causal inference and “built-in” generalizability by leveraging today’s intelligent agent, virtual environments, and other technologies. In SRD, a “default control group” (DCG) can be created in a virtual environment by representatively sampling from real-world situations. Experimental groups can be built with systematic manipulations onto the DCG base. Applying systematic design features (e.g., random assignment to DCG versus experimental groups) in SRD affords valid causal inferences. After explicating the proposed SRD synthesis, we delineate how the approach concurrently advances generalizability and robustness, cause-effect inference and precision science, a computationally-enabled cumulative psychological science supporting both “bigger theory” and concrete implementations grappling with tough questions (e.g., what is context?) and affording rapidly-scalable interventions for real-world problems.

中文翻译：

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可推广环境中的因果推理：系统代表性设计

摘要 因果推理和概括性都很重要。从历史上看，系统设计强调因果推理，而代表性设计则注重普遍性。在这里，我们建议进行变革性综合——系统代表性设计（SRD）——通过利用当今的智能代理、虚拟环境和其他技术，同时增强因果推理和“内置”普遍性。在SRD中，可以通过从现实世界情况中进行代表性采样来在虚拟环境中创建“默认控制组”（DCG）。可以在 DCG 基础上通过系统操作建立实验组。在 SRD 中应用系统设计特征（例如，随机分配到 DCG 与实验组）可以提供有效的因果推论。在解释了所提出的 SRD 综合之后，我们描述了该方法如何同时提高普遍性和鲁棒性、因果推理和精确科学，这是一种支持“更大理论”和解决棘手问题的具体实现的计算支持的累积心理科学（例如，什么）是上下文？）并为现实世界的问题提供快速可扩展的干预措施。