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Interpretation of Linear Regression Coefficients under Mean Model Miss-Specification

Linear regression is a frequently used tool in statistics, however, its validity and interpretability relies on strong model assumptions. While robust estimates of the coefficients' covariance extend the validity of hypothesis tests and confidence intervals, a clear interpretation of the coefficients is lacking if the mean structure of the model is miss-specified. We therefore suggest a new intuitive and mathematical rigorous interpretation of the coefficients that is independent from specific model assumptions. It relies on a new population based measure of association. The idea is to quantify how much the population mean of the dependent variable Y can be changed by changing the distribution of the independent variable X. Restriction to linear functions for the distributional changes in X provides the link to linear regression. It leads to a conservative approximation of the newly defined and generally non-linear measure of association. The conservative linear approximation can then be estimated by linear regression. We show how this interpretation can be extended to multiple regression and how far and in which sense it leads to an adjustment for confounding. We point to perspectives for new analysis strategies and illustrate the utility and limitations of the new interpretation and strategies by examples and simulations.