Ensuring Causal, Not Casual, Inference
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With innovation in causal inference methods and a rise in non-experimental data availability, a growing number of prevention researchers and advocates are thinking about causal inference. In this commentary, we discuss the current state of science as it relates to causal inference in prevention research, and reflect on key assumptions of these methods. We review challenges associated with the use of causal inference methodology, as well as considerations for hoping to integrate causal inference methods into their research. In short, this commentary addresses the key concepts of causal inference and suggests a greater emphasis on thoughtfully designed studies (to avoid the need for strong and potentially untestable assumptions) combined with analyses of sensitivity to those assumptions.
KeywordsCausal inference Randomized controlled trials Assumptions Mediation
The authors thank Wolfgang Wiedermann for the invitation to submit this commentary.
Dr. Stuart’s work on this commentary was supported by the National Institute of Mental Health, R01MH115487 (PI: Stuart).
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Because this article is a commentary, informed consent is not applicable.
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