addnl wording to compare causal and prediction

notebooks/Solutions/Causal Interpretation for Ames Housing Price.ipynb

@@ -2279,6 +2279,13 @@
     "global_summ.sort_values(by=\"p_value\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The table above sorts the features by causal significance (p value). If we compare the causal summary table to the ordering of the SHAP feature importances plot, we notice that for the most part the top predictive features are also causally significant. For example, OverallQual is the most predictive feature according to SHAP, and is also the most causally signficant. However, in contrast, AgeAtSale is a good predictor, but not very causally significant. This could perhaps be explained by the idea that even though older houses are correlated with low quality characteristics, once you control for these characteristics, house age by itself is not causally significant.   "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 18,