diff --git a/docs/examples/qini_curves_for_costly_treatment_arms.ipynb b/docs/examples/qini_curves_for_costly_treatment_arms.ipynb
index 89e348c2..3ccb4eeb 100644
--- a/docs/examples/qini_curves_for_costly_treatment_arms.ipynb
+++ b/docs/examples/qini_curves_for_costly_treatment_arms.ipynb
@@ -7,7 +7,25 @@
    "source": [
     "# Qini curves with multiple costly treatment arms\n",
     "\n",
-    "This notebook gives a brief overview of Qini curves for multi-armed treatment rules and a simple simulated example."
+    "This notebook shows approaches to evaluating multi-armed CATE estimators from `causalML` with the Multi-Armed Qini metric available in the `maq` package (available at https://github.com/grf-labs/maq).\n",
+    "\n",
+    "\n",
+    "This metric is a generalization of the familiar *Qini curve* to settings where we have multiple treatment arms available, and the cost of assigning treatment can vary by both unit and treatment arm according to some known cost structure. At a high level, this metric essentially allows you to quantify the value of targeting with more treatment arms by undertaking a cost-benefit exercise that uses your CATE estimates to assign the arm to the unit that is most cost-beneficial at various budget constraints.\n",
+    "\n",
+    "This notebook gives a brief overview of the statistical setup and a walkthrough with a simple simulated example. \n",
+    "\n",
+    "\n",
+    "To use this functionality, you first have to install the `maq` Python package from GitHub. The latest source release can be installed with:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a633fa7",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "pip install \"git+https://github.com/grf-labs/maq.git#egg=maq&subdirectory=python-package\"\n",
+    "```"
    ]
   },
   {
@@ -22,7 +40,7 @@
     "from xgboost import XGBRFRegressor\n",
     "\n",
     "# Generalized Qini curves\n",
-    "from causalml.metrics import MAQ, get_ipw_scores\n",
+    "from maq import MAQ, get_ipw_scores\n",
     "\n",
     "import numpy as np\n",
     "np.random.seed(42)"