Here, we provide a brief overview of a technique that may hold promise for scholars working on key criminological and criminal justice topics.
We provide an abbreviated overview of Mendelian randomization (MR), a newer variant of instrumental variables analysis, facilitated by expanding genomic technology worldwide. Our goal is to offer readers, unacquainted with the topic, a quick checklist of key assumptions, considerations, shortcomings, and practical applications of the technique.
The causal inference capabilities of the design seem poised to continue pushing modern crime science forward, assuming that careful attention is payed to key assumptions of the technique.
Researchers interested in causality as it relates to antisocial behaviors may benefit by the addition of MR to the toolkit alongside other data analysis tools. This strategy also offers an avenue for cross-collaboration with scientists working in other fields, thus expanding the breadth of expertise contributing to an important societal subject in crime.
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In a courtesy review of earlier drafts of this paper, a colleague pointed out to us a key point also worth considering. In particular, IV analysis in the social sciences is often performed by researchers utilizing macro-level data. This is important in this case because macro-level analyses often have r-squared values that tend to be considerably larger than what might be observed in either individual-level analyses or in genomic analyses.
We would like to thank an anonymous reviewer for reminding us of this point, and for urging us to consider it in the revised version of the manuscript.
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Boutwell, B.B., Adams, C.D. A research note on Mendelian randomization and causal inference in criminology: promises and considerations. J Exp Criminol (2020). https://doi.org/10.1007/s11292-020-09436-9
- Causal inference
- Genome-wide association studies
- Instrumental variables
- Mendelian randomization
- Research note