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An important recent preprint by Griffith et al highlights how 'collider bias'in studies of COVID19 undermines our understanding of the disease risk andseverity. This is typically caused by the data being restricted to people whohave undergone COVID19 testing, among whom healthcare workers areoverrepresented. For example, collider bias caused by smokers beingunderrepresented in the dataset may (at least partly) explain empirical resultsthat suggest smoking reduces the risk of COVID19. We extend the work ofGriffith et al making more explicit use of graphical causal models to interpretobserved data. We show that their smoking example can be clarified and improvedusing Bayesian network models with realistic data and assumptions. We show thatthere is an even more fundamental problem for risk factors like 'stress' which,unlike smoking, is more rather than less prevalent among healthcare workers; inthis case, because of a combination of collider bias from the biased datasetand the fact that 'healthcare worker' is a confounding variable, it is likelythat studies will wrongly conclude that stress reduces rather than increasesthe risk of COVID19. Indeed, "being in close contact with COVID19 people"reduces the risk of COVID19. To avoid such potentially erroneous conclusions,any analysis of observational data must take account of the underlying causalstructure including colliders and confounders. If analysts fail to do thisexplicitly then any conclusions they make about the effect of specific riskfactors on COVID19 are likely to be flawed.

A note on 'Collider bias undermines our understanding of COVID-19  disease risk and severity' and how causal Bayesian networks both expose and  resolve the problem