Model Interpretation¶

Association \(\ne\) Causation

Classification of Inference Techniques¶

	IDK	Scope		Limitations
Simple Linear Regression \(y = \beta_0 + \beta_j x_j + \beta_\text{ind} G + \beta_\text{int} x_j G\)	Model-Specific	Global	\(\beta_0\) is the baseline value of \(y\) when \(x_j=0\) \(\beta_j\) is the change in \(y\) for every unit increase in \(x_j\) \(\beta_\text{ind}\) is the change in baseline for group \(G\), ie baseline will now be \((\beta_0 + \beta_\text{ind})\) \(\beta_\text{int}\) is the additional change in \(y\) in group \(G\), ie for every unit increase in \(x_j\), \(y\) changes by \((\beta_j + \beta_\text{int})\) units for group \(G\)
\(\ln \vert y \vert = \beta_0 + \beta_j x_j\)	Model-Specific	Global	For every unit increase in \(x_j\), percentage change in \(y\) is \(\beta_j\) units
\(\ln \vert y \vert = \beta_0 + \beta_j \ln \vert x_j \vert\)	Model-Specific	Global	Elasticity of \(y\) wrt \(x_j\) is given by \(\beta_j\) \(\beta_j = \dfrac{\% \Delta y}{\% \Delta x_j}\)
SAGE	Model-Agnostic	Global
Variable/Feature Importance	Model-Agnostic	Global	Decrease of in-sample error due to splits over \(x\), averaged over all trees of ensemble
Partial Dependence	Model-Agnostic	Global	Partial derivative of \(y\) wrt \(x\): Marginal effect of \(x\) on \(y\) after integrating out all other vars
SHAP	Model-Agnostic	Local		Does not account for interactions
SHAP-Interactions	Model-Agnostic	Local
LIME	Model-Agnostic	Local

2025-07-26