Important considerations in regression modeling
This section outlines a set of discussion points that we should bring up. You will face them as you model regression equations to support finding evidence of causality. Let’s start!
Which covariates to consider in the model?
The question of whether to control for all available covariates in causal analysis is nuanced, reflecting deep considerations around the potential for confounding, the relevance of variables, and the structural relationships among them. The primary concern is that while controlling for variables (covariates) aims to mitigate confounding, this strategy does not uniformly lead to more accurate causal conclusions. The rationale often cited for extensive control is to yield more conservative hypothesis tests. However, this can backfire, as controlling for the wrong variables may introduce spurious effects or even reverse the signs of effects, leading to erroneous conclusions about causal relationships.
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