Regression with Confounding Variables
Isolating the effect of a variable
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I want to quantify the effect of [VARIABLE_X] on [VARIABLE_Y] but I suspect confounding variables: [LIST_OF_CONFOUNDERS]. My dataset has [SIZE] observations. Explain how to control for confounders using multiple regression, propensity score matching, and how to interpret adjusted coefficients.
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Pensez à tester la multicolinéarité (VIF > 5) avant d’interpréter les coefficients, et standardisez les variables si vous voulez comparer leurs effets. Pour le PSM, vérifiez le chevauchement des scores de propension : sans support commun, l’estimation est fragile. Enfin, l’interprétation des coefficients ajustés n’est causale que si tous les confondants sont mesurés et inclus.
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