Causal Inference in Generalizable Environments: Systematic Representative Design.
Causal Inference in Generalizable Environments: Systematic Representative Design.
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DOI：
10.1080/1047840x.2019.1693866
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发表时间：
2019
影响因子：
9.3
通讯作者：
Read SJ
中科院分区：
文献类型：
--
作者：
Miller LC;Shaikh SJ;Jeong DC;Wang L;Gillig TK;Godoy CG;Appleby PR;Corsbie-Massay CL;Marsella S;Christensen JL;Read SJ
因果推理和可概括性都很重要。从历史上看，系统设计强调因果推理，而代表性设计则侧重于概括性。在这里，我们提出了一种变革性的综合--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”概括性。在SRD中，可以通过从真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）。实验组可以通过对DCG基础的系统操作来建立。应用系统设计特征（例如，随机分配到DCG与实验组）在SRD中提供有效的因果推断。在阐述了所提出的SRD综合之后，我们描述了该方法如何同时推进概括性和鲁棒性，因果推理和精确科学，这是一种支持“更大理论”和解决坚韧问题的具体实现的计算支持的累积心理科学（例如，什么是context？）并为现实世界的问题提供快速可扩展的干预措施。
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.