Prevention Science

, Volume 20, Issue 3, pp 452–456 | Cite as

Ensuring Causal, Not Casual, Inference

  • Rashelle J. MusciEmail author
  • Elizabeth Stuart


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.


Causal 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.

Ethical Approval

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.

Informed Consent

Because this article is a commentary, informed consent is not applicable.


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Copyright information

© Society for Prevention Research 2018

Authors and Affiliations

  1. 1.Department of Mental HealthJohns Hopkins University Bloomberg School of Public HealthBaltimoreUSA

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