Confounding
Everything on Aqrab tagged Confounding — grouped into one landing page so readers can go deeper by problem family instead of bouncing around the archive blind.
Covariate Balance Diagnostics: When the Love Plot Says Balanced but the Groups Still Differ
A practical guide to covariate balance diagnostics after propensity-score matching or weighting. Covers why a Love plot of standardized mean differences can read balanced while distributions differ in spread and tails, why you should check variance ratios and distributional distances and interactions, why you should never significance-test balance because the p-value tracks sample size, and what reviewers should demand.
Simpson's Paradox: When Every Subgroup Says One Thing and the Total Says the Opposite
A practical guide to Simpson's paradox for clinical researchers. Covers why a treatment can help in every subgroup yet look harmful pooled, why 'always stratify' is wrong, and how the causal role of the stratifier — confounder, mediator, or collider — decides which table to trust.
The Table 2 Fallacy: When Every Adjusted Coefficient Looks Like a Cause
A practical guide to the Table 2 fallacy for clinical researchers. Covers why secondary coefficients in an adjusted model are not causal effects, mutual adjustment, mediator and confounder mismatch, and what reviewers should demand before reading a covariate row as a finding.
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