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. (shrink)

The dual role of microbial communities as either beneficial/functional or harmful/pathogenic involves two issues concerning causality in physiology and medicine, etiology of disease, and the notion of function in biology. Causal explanation formulated by the germ theory of disease and the Koch postulates connects the existence of a specifically identified microbe to disease by the isolation and identification of a pathogen from an organism with the disease and the successful infection of a healthy individual. Similarly, microbiome research in medicine centers (...) on taxonomic composition in correlation with physiological conditions and germ-free mice experimentations to establish causal connections. However, because the microbiome is an ecological community, it lacks the specificity of identification assumed in the germ theory. Furthermore, the ecological aspects within and between microbiomes such as background conditions, microbial interactions, and interdependence, are treated as confounding. Looking at ecological studies, the causal explanations are less specific regarding complex systems with their processes of interactions. This article makes the following two points: One is that microbiome causality in the host should be understood similarly to the microbiome causality in an ecosystem. The second is that if considering a microbiome’s causality in the host as an ecosystem, its etiological explanation needs to be revisited to include a heterogeneous and mutual notion of interactions. Finally, according to the second point, the notion of function in ecology with its relevant notion of causality should be considered accordingly. (shrink)

Social scientists appeal to various “structures” in their explanations including public policies, economic systems, and social hierarchies. Significant debate surrounds the explanatory relevance of these factors for various outcomes such as health, behavioral, and economic patterns. This paper provides a causal account of social structural explanation that is motivated by Haslanger (2016). This account suggests that social structure can be explanatory in virtue of operating as a causal constraint, which is a causal factor with unique characteristics. A novel causal framework (...) is provided for understanding these explanations–this framework addresses puzzles regarding the mysterious causal influence of social structure, how to understand its relation to individual choice, and what makes it the main explanatory (and causally responsible) factor for various outcomes. (shrink)