diff --git a/manuscript/05.9b-agnostic-shap.Rmd b/manuscript/05.9b-agnostic-shap.Rmd
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--- a/manuscript/05.9b-agnostic-shap.Rmd
+++ b/manuscript/05.9b-agnostic-shap.Rmd
@@ -195,7 +195,7 @@ If we add an L1 penalty to the loss L, we can create sparse explanations.
 
 ### TreeSHAP
 
-Lundberg et al. (2018)[^tree-shap] proposed TreeSHAP, a variant of SHAP for tree-based machine learning models such as decision trees, random forests and gradient boosted trees.
+Lundberg et al. (2018)[^treeshap] proposed TreeSHAP, a variant of SHAP for tree-based machine learning models such as decision trees, random forests and gradient boosted trees.
 TreeSHAP was introduced as a fast, model-specific alternative to KernelSHAP, but it turned out that it can produce unintuitive feature attributions.
 
 TreeSHAP defines the value function using the conditional expectation $E_{X_S|X_C}(\hat{f}(x)|x_S)$ instead of the marginal expectation.