🤖 AI Summary
Traditional heritability concepts rely on strong assumptions and lack an explicit causal model, making it difficult to clearly disentangle the causal contributions of genes and environment to phenotypic traits. This work introduces a counterfactual causal framework into the definition of heritability for the first time, formulating a novel heritability measure based on the potential outcomes model. By comparing each individual with their counterfactual “non-identical twin” under identical environmental conditions, the approach explicitly articulates causal structural assumptions and derives bounds on heritability that are computable from observational data alone. The study systematically contrasts conventional heritability estimates derived from population studies, twin/sibling designs, and plant breeding, uncovering their implicit causal assumptions and providing a more rigorous quantitative foundation for the nature–nurture debate.
📝 Abstract
Heritability is a central concept in the long-standing debate about nature versus nurture in biological and social sciences. However, existing notions of heritability are based on strong assumptions and do not use explicit causal models. We propose a new, counterfactual definition of heritability by adopting the potential outcomes model in causal inference. Our counterfactual heritability measures the importance of genetic inheritance by the average magnitude of difference between an individual with their hypothetical ``non-identical twin'' that is exposed to the exact same environment. We provide bounds on the counterfactual heritability that can, in principle, be computed from observational data. We then compare counterfactual heritability and its associated bounds with common notions of heritability in population-based studies, twin and sibling studies, and plant breeding experiments. Our results and comparisons highlight the importance of clarifying the causal structural assumptions and counterfactual comparisons in reasoning about heritability.