Causal Inference in Generalizable Environments: Systematic Representative Design

Lynn C. Miller, Sonia Jawaid Shaikh, David C. Jeong, Liyuan Wang, Traci K. Gillig, Carlos G. Godoy, Paul R. Appleby, Charisse L. Corsbie-Massay, Stacy Marsella, John L. Christensen, Stephen J. Read

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)173-202
Number of pages30
JournalPsychological Inquiry
Issue number4
StatePublished - Oct 2 2019


  • Brunswik
  • cause and effect
  • experimental design
  • games
  • generalizability
  • representative design
  • systematic design
  • systematic representative design
  • virtual environments
  • virtual reality

ASJC Scopus subject areas

  • General Psychology


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