Posts tagged confounding

Sensitivity Analysis for Unmeasured Confounding

All applied inference is argument. Against every experiment you can set the contention that the working conditions were imperfect. Some aspect of the evaluation was flawed. Maybe treatment assignment introduced a subtle kind of bias, or the subjects didn’t comply fully with the design. Against every experiment you can contrast the scientific ideal of perfect randomisation and clear adherence. Holding an experiment against that ideal is due diligence. Sensitivity analysis does it systematically, by varying how far the working conditions fall short of perfect randomisation.

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Elemental Confounds

This notebook is part of the PyMC port of the Statistical Rethinking 2023 lecture series by Richard McElreath.

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