Abstract
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.
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Acknowledgements
The authors thank Wolfgang Wiedermann for the invitation to submit this commentary.
Funding
Dr. Stuart’s work on this commentary was supported by the National Institute of Mental Health, R01MH115487 (PI: Stuart).
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Musci, R.J., Stuart, E. Ensuring Causal, Not Casual, Inference. Prev Sci 20, 452–456 (2019). https://doi.org/10.1007/s11121-018-0971-9
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DOI: https://doi.org/10.1007/s11121-018-0971-9