Coherence of “genetics” or “biomarker” as a single causal variable

Determine the extent to which genetic factors or biological biomarker variables can be represented as a single coherent common cause in causal graphs for neuroimaging mega-studies that relate batch/site assignment, neuroanatomy, and measured connectomes, and specify when such constructs must instead be modeled as multiple distinct causal factors to appropriately block backdoor paths in causal analyses.

Background

In the discussion of unobserved confounding, the paper considers a causal graph where an unmeasured biomarker (e.g., genetics) may confound the relationship between batch/site assignment and measured connectomes through neuroanatomy. The authors note that including neuroanatomy in an adjustment set can block certain backdoor paths but emphasize that this example is simplified.

They explicitly highlight uncertainty about whether broad constructs like “genetics” or “biomarker” should be treated as a single coherent causal variable or instead decomposed into multiple causal factors that influence exposure and outcome through different pathways. This modeling choice has direct implications for whether observed covariates can suffice to block backdoor paths and for the identifiability of causal effects.