Dataset Information

Causal Inference in Generalizable Environments: Systematic Representative Design.

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.

SUBMITTER: Miller LC

PROVIDER: S-EPMC7577318 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Causal Inference in Generalizable Environments: Systematic Representative Design.

Miller Lynn C LC Shaikh Sonia Jawaid SJ Jeong David C DC Wang Liyuan L Gillig Traci K TK Godoy Carlos G CG Appleby Paul R PR Corsbie-Massay Charisse L CL Marsella Stacy S Christensen John L JL Read Stephen J SJ

Psychological inquiry 20190101 4

Causal inference and generalizability <i>both</i> matter. Historically, systematic designs emphasize causal inference, while representative designs focus on generalizability. Here, we suggest a transformative synthesis - <i>Systematic Representative Design (SRD)</i> - 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 ...[more]

PMID: 33093760

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Project description:BACKGROUND:The global burden of disease has shifted from communicable diseases in children to chronic diseases in adults. This epidemiologic shift varies greatly by region, but in Europe, chronic conditions account for 86% of all deaths, 77% of the disease burden, and up to 80% of health care expenditures. A number of risk factors have been implicated in chronic diseases, such as exposure to infectious agents. A number of associations have been well established while others remain uncertain. METHODS AND FINDINGS:We assessed the body of evidence regarding the infectious aetiology of chronic diseases in the peer-reviewed literature over the last decade. Causality was assessed with three different criteria: First, the total number of associations documented in the literature between each infectious agent and chronic condition; second, the epidemiologic study design (quality of the study); third, evidence for the number of Hill's criteria and Koch's postulates that linked the pathogen with the chronic condition. We identified 3136 publications, of which 148 were included in the analysis. There were a total of 75 different infectious agents and 122 chronic conditions. The evidence was strong for five pathogens, based on study type, strength and number of associations; they accounted for 60% of the associations documented in the literature. They were human immunodeficiency virus, hepatitis C virus, Helicobacter pylori, hepatitis B virus, and Chlamydia pneumoniae and were collectively implicated in the aetiology of 37 different chronic conditions. Other pathogens examined were only associated with very few chronic conditions (? 3) and when applying the three different criteria of evidence the strength of the causality was weak. CONCLUSIONS:Prevention and treatment of these five pathogens lend themselves as effective public health intervention entry points. By concentrating research efforts on these promising areas, the human, economic, and societal burden arising from chronic conditions can be reduced.

Dataset Information

Causal Inference in Generalizable Environments: Systematic Representative Design.

Publications

Causal Inference in Generalizable Environments: Systematic Representative Design.

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