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Lost without a cause: time to embrace causal thinking using Directed Acyclic Graphs (DAGs)
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Lost without a cause: time to embrace causal thinking using Directed Acyclic Graphs (DAGs)
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Journal Article
Lost without a cause: time to embrace causal thinking using Directed Acyclic Graphs (DAGs)
2023
Overview
[...]we often need to turn to quasi-experimental and observational studies to gain insight into these causal effects. [...]policies based on these incorrect causal interpretations were implemented, which potentially increased the risk for COVID-19 among vulnerable populations. Because the unobserved potential outcome of an individual cannot be known, researchers often compare the average outcomes of exposed and unexposed groups. [...]the estimate for smoking only takes into account the direct effect of smoking on death from COVID-19 and dismisses the indirect effect via chronic respiratory disease, leading to an underestimation of the total causal effect of smoking on death from COVID-19. [...]the effect estimate is also biased by a spurious pathway via unmeasured confounders (U), which could, for example, include a gene or air pollution that are influencing both risk for chronic respiratory disease and death from COVID-19.
