Describing, Resampling, Validating, and Simplifying the Model

Abstract

Before addressing issues related to describing and interpreting the model and its coefficients, one can never apply too much caution in attempting to interpret results in a causal manner. causal inferenceRegression models are excellent tools for estimating and inferring associations between an X and Y given that the “right” variables are in the model. Any ability of a model to provide causal inference rests entirely on the faith of the analyst in the experimental design, completeness of the set of variables that are thought to measure confounding and are used for adjustment when the experiment is not randomized, confoundinglack of important measurement error, and lastly the goodness of fit of the model.