Explanatory Modeling
Explanatory modeling is a statistical and data analysis approach focused on understanding and interpreting the relationships between variables, often to test hypotheses or explain causal mechanisms. It emphasizes model interpretability, statistical inference, and theoretical grounding, commonly using techniques like linear regression, ANOVA, or structural equation modeling. This contrasts with predictive modeling, which prioritizes forecasting accuracy over understanding underlying processes.
Developers should learn explanatory modeling when working on projects that require understanding why phenomena occur, such as in scientific research, A/B testing analysis, or business intelligence to inform decision-making. It is essential in fields like economics, social sciences, and healthcare, where interpreting model coefficients and assessing causal effects is critical for drawing valid conclusions and driving policy or strategy.