Causal Inference and Observational Research: The Utility of Twins
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Valid causal inference is central to progress in theoretical and applied psychology. Although the randomized experiment is widely considered the gold standard for determining whether a given exposure increases the likelihood of some specified outcome, experiments are not always feasible and in some cases can result in biased estimates of causal effects. Alternatively, standard observational approaches are limited by the possibility of confounding, reverse causation, and the nonrandom distribution of exposure (i.e., selection). We describe the counterfactual model of causation and apply it to the challenges of causal inference in observational research, with a particular focus on aging. We argue that the study of twin pairs discordant on exposure, and in particular discordant monozygotic twins, provides a useful analog to the idealized counterfactual design. A review of discordant-twin studies in aging reveals that they are consistent with, but do not unambiguously establish, a causal effect of lifestyle factors on important late-life outcomes. Nonetheless, the existing studies are few in number and have clear limitations that have not always been considered in interpreting their results. It is concluded that twin researchers could make greater use of the discordant-twin design as one approach to strengthen causal inferences in observational research.
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Monozygotic twins share both a common genotype and a similar rearing environment.
“Monozygotic twins share both a common genotype and a similar rearing environment.”
From Life Events and Personality Trait Change. (2018) · cites this paper as McGue, Osler, & Christensen, 2010
The active comparator design adjusted for confounding from all factors shared across individuals initiating analgesic treatment with opioids and with NSAIDs.
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From Association of opioid prescription initiation during adolescence and young adulthood with subsequent substance-related morbidity (2020) · cites this paper as [35]
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“Regarding the within pair differences in testosterone and MRT performance, we observed the negative correlation only among dizygotic twin pairs, but not in monozygotic twin pairs, which suggests possible shared genetics effects between these two phenotypes (McGue et al., 2010).”
BackgroundImpact of Hip Fracture on Mortality: A Cohort Study in Hip Fracture Discordant Identical Twins
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- Authors
- McGue, M., Osler, M., & Christensen, K.
- Affiliations
- University of Minnesota (US)
- University of Southern Denmark (DK)
- Corresponding author
- McGue, Matt
- Venue
- Perspectives on Psychological Science
- Publisher
- SAGE Publications
- Published
- 2010-09
- Volume
- 5
- Issue
- 5
- Pages
- 546-556
- ISSN
- 1745-6916 (ISSN-L)
- 1745-6924
- PMC
- PMC3094752
- OpenAlex
- W2024702358
- Cited by
- 529
- References
- 94
- Citations by year
- 2026: 34 · 2025: 40 · 2024: 40 · 2023: 45 · 2022: 40 · 2021: 47 · 2020: 36 · 2019: 37 · 2018: 23 · 2017: 32 · 2016: 42 · 2015: 25 · 2014: 28 · 2013: 23 · 2012: 25
- FWCI
- 2.67
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Perspectives on Psychological Science (publishedVersion)
- PubMed (publishedVersion)
- University of Southern Denmark Research Portal (University of Southern Denmark) (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Alcohol Abuse and Alcoholism (R01AA009367, R37AA009367)
- National Institute on Aging (P01AG008761)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Keywords
- causal inference, observational studies, discordant twins, monozygotic twins, aging, confounding, reverse causality, selection bias, lifestyle factors, causal effects
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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