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#106 Active Statistics, Two Truths & a Lie, with Andrew Gelman

Learning Bayesian Statistics

NOTE

Causality Requires Comparison

Causal inference in statistical models must be approached through careful comparisons rather than definitive statements. When interpreting regression coefficients, it’s essential to emphasize that any observed relationships are conditional on specific variables, such as sex or other predictors. This includes specifying that comparisons are made between individuals who are similar except for the variable in question, ensuring that interpretations reflect average outcomes under the model rather than implying direct causation. Therefore, clear language that acknowledges these conditions is crucial in statistical communication.

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