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Building causal knowledge in behavior genetics

Published online by Cambridge University Press:  05 May 2022

James W. Madole Open the ORCID record for James W. Madole [Opens in a new window]  and
K. Paige Harden
James W. Madole
Affiliation:
Department of Psychology, University of Texas at Austin, Austin, TX, USA jmadole@utexas.edu harden@utexas.edu VA Puget Sound Health Care System, Seattle, WA, USA
K. Paige Harden
Affiliation:
Department of Psychology, University of Texas at Austin, Austin, TX, USA jmadole@utexas.edu harden@utexas.edu
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Abstract

Behavior genetics is a controversial science. For decades, scholars have sought to understand the role of heredity in human behavior and life-course outcomes. Recently, technological advances and the rapid expansion of genomic databases have facilitated the discovery of genes associated with human phenotypes such as educational attainment and substance use disorders. To maximize the potential of this flourishing science, and to minimize potential harms, careful analysis of what it would mean for genes to be causes of human behavior is needed. In this paper, we advance a framework for identifying instances of genetic causes, interpreting those causal relationships, and applying them to advance causal knowledge more generally in the social sciences. Central to thinking about genes as causes is counterfactual reasoning, the cornerstone of causal thinking in statistics, medicine, and philosophy. We argue that within-family genetic effects represent the product of a counterfactual comparison in the same way as average treatment effects (ATEs) from randomized controlled trials (RCTs). Both ATEs from RCTs and within-family genetic effects are shallow causes: They operate within intricate causal systems (non-unitary), produce heterogeneous effects across individuals (non-uniform), and are not mechanistically informative (non-explanatory). Despite these limitations, shallow causal knowledge can be used to improve understanding of the etiology of human behavior and to explore sources of heterogeneity and fade-out in treatment effects.

Keywords

behavior geneticscausal inferencecounterfactual reasoningexperimental designsindividual differencesphilosophy of science

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Behavioral and Brain Sciences , Volume 46 , 2023 , e182
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Copyright © The Author(s), 2022. Published by Cambridge University Press

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