Causation, not collinearity: identifying sources of bias when modelling the evolution of brain size and other allometric traits

Sam Froman Walmsley*, Michael Morrissey

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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.
Original languageEnglish
Pages (from-to)234-244
Number of pages11
JournalEvolution Letters
Volume6
Issue number3
Early online date9 Nov 2021
DOIs
Publication statusPublished - Jun 2022

Keywords

  • Allometry
  • Brain size
  • Causal inference
  • Coevolution
  • Comparative methods
  • Correlated response to selection
  • Receiprocal evolution

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