Causal Inference in Generalizable Environments: Systematic Representative Design

Lynn C. Miller

doi:10.1080/1047840X.2019.1693866

Causal Inference in Generalizable Environments: Systematic Representative Design

2020-01-04

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Charisse L’Pree Corsbie-Massay

DOI：10.1080/1047840X.2019.1693866

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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.

Keywords:

Brunswik

cause and effect

experimental design

games

generalizability

representative design

systematic design

systematic representative design

virtual environments

virtual reality

Causal Inference in Generalizable Environments: Systematic Representative Design

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