Limitations of individual causal models, causal graphs, and ignorability assumptions, as illustrated by random confounding and design unfaithfulness

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Robust causal inference using directed acyclic graphs: the R package 'dagitty'. (2017, methodology): “…that reﬂects the way the DAGitty web application is typically used in epidemiology: a researcher drawing a DAG to determine which covariate adjustment set(s) are required to remove structural confounding bias (4,13); and therefore which covariates should be measured / included (see Figure 1A).”
For and Against Methodologies: Some Perspectives on Recent Causal and Statistical Inference Debates (2017, background): “Such perverse incentives have typically been countered by bias (shrinkage) toward the null, as crudely implemented by null testing, and more defensibly as null penalization (Greenland and Mansournia 2015b).”
Errors in causal inference: an organizational schema for systematic error and random error. (2016, background): “In introductory epidemiology textbooks [3, 4], the distinction between them is usually explained using a schematic illustration of target shooting.”