Models as Prediction Machines: How to Convert Confusing Coefficients Into Clear Quantities

Abstract

Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models but is challenging for more complex models with, for example, categorical variables, interactions, nonlinearities, or hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in consistent fashion to draw inferences from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports more than 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational studies, randomized experiments) to answer different research questions (e.g., about associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modeling ordinal outcomes, and interpreting nonlinear models).