Illustrating bias due to conditioning on a collider.

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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).”
Reporting guidelines for Human Microbiome Research: the STORMS checklist (2021, background): “Justification should be provided for variables included or excluded in regression models for causal inference (item 6.0), as adjusting for or stratifying on a non-confounding variable can introduce bias 34 .”
A review of instrumental variable estimators for Mendelian randomization (2015, background): “A collider is a variable that is influenced both by the exposure or a causal consequent thereof and by the outcome or a causal consequent thereof.(111) There may also be bias if the analysis model including the covariate is misspecified.”
Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry (2018, background): “We found that the majority of WHRadjBMI signals identified has genuine effect on body shape and that any bias caused by adjusting WHR for a correlated covariate (14,15) (that is, BMI) was minimal.”
Mendelian randomisation for mediation analysis: current methods and challenges for implementation (2019, neutral): “3) [6, 32, 33].”