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The 'Table 2 Fallacy' is a term coined by Daniel Westreich and Sander Greenland in 2013.[1]. It is a concept in causal inference.
Table 2 Fallacy: The misconception that the associations between confounders and the outcome can be interpreted as valid estimates of causal associations between each confounder and the outcome.
Maarten van Smeden, A Very Short List of Common Pitfalls in Research Design, Data Analysis, and Reporting, https://journals.stfm.org/primer/2022/van-smeden-2022-0059/
In scientific papers reporting observational studies, people often report both crude and adjusted associations between variables included in a regression model and the outcome of interest in the second table. If the purpose of the analysis is causal inference, usually, the variables one would choose to adjust for will differ for each exposure - outcome pairing. The Table 2 Fallacy occurs when people seek to give a causal interpretation to the other parameters estimated using a multivariable regression model that was only designed to explore a single exposure - outcome association.