SHAP Interaction Values

A standard SHAP value tells you how much each feature contributed to a single prediction, but it collapses any interaction between two features into their individual scores, hiding the fact that a feature's contribution might depend heavily on the value of another feature. SHAP interaction values fix this by splitting a prediction's total attribution into a main effect for each feature plus a separate interaction term for every pair of features, based on the same underlying Shapley-value math applied to pairs instead of singletons.

The result is a matrix rather than a single vector per prediction: diagonal entries are ordinary main effects, and off-diagonal entries capture how much two features jointly move the prediction beyond what each does alone. This is far more informative when two variables genuinely interact, like momentum only mattering in low-volatility regimes, but it is also more expensive to compute, since the number of pairs grows quadratically with the number of features.

Worked example

A model predicts stock returns using momentum and realized volatility. Ordinary SHAP values might show momentum contributing +0.4% and volatility contributing +0.1% to a given prediction. The SHAP interaction value between the two might reveal an additional +0.3% coming specifically from momentum being high while volatility is low, a joint effect invisible in the individual scores, and one that would disappear if either feature were examined on its own.