ArticleEvolution letters2022
Causation, not collinearity: Identifying sources of bias when modelling the evolution of brain size and other allometric traits.
Article in Evolution letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Socioecological complexity in primate groups and its cognitive correlates.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Pooled it
- Tetrapod sperm length evolution in relation to body mass is shaped by multiple trade-offs.Nature communications · 2024Article
- Estimating the relationship between fitness and metabolic rate: which rate should we use?Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- Using relative brain size as predictor variable: Serious pitfalls and solutions.Ecology and evolution · 2022Article
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Authors and funding
2 authors.
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Abstract
Many biological traits covary with body size, resulting in an allometric relationship. Identifying the evolutionary drivers of these traits is complicated by possible relationships between a candidate selective agent and body size itself, motivating the widespread use of multiple regression analysis. However, the possibility that multiple regression may generate misleading estimates when predictor variables are correlated has recently received much attention. Here, we argue that a primary source of such bias is the failure to account for the complex causal structures underlying brains, bodies, and agents. When brains and bodies are expected to evolve in a correlated manner over and above the effects of specific agents of selection, neither simple nor multiple regression will identify the true causal effect of an agent on brain size. This problem results from the inclusion of a predictor variable in a regression analysis that is (in part) a consequence of the response variable. We demonstrate these biases with examples and derive estimators to identify causal relationships when traits evolve as a function of an existing allometry. Model mis-specification relative to plausible causal structures, not collinearity, requires further consideration as an important source of bias in comparative analyses.
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