Graphical Representation of Missing Data Problems

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Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data (2017, methodology): “For example, Turkheimer and Harden (2014) investigated whether religiosity has a causal effect on delinquency and found a negative correlation when they simply correlated the variables across their whole sample.”
Accounting for missing data in statistical analyses: multiple imputation is not always the answer (2019, background): “Causal diagrams, which depict assumed causal relationships between two or more variables,(31) can be used to depict assumptions about missingness mechanisms.(32,33) Figure 1 shows diagrams of six possible mechanisms for a simple scenario where only cannabis use has missing data.”
A Primer on Inverse Probability of Treatment Weighting and Marginal Structural Models (2016, background): “This type of collider bias has been explored in the causal inference literature (Cole et al., 2010; Greenland, 2003) and recently also in the missing data literature (Mohan, Pearl, & Tian, 2013; Thoemmes & Mohan, 2015; Thoemmes & Rose, 2014).”