diff --git a/docs/source/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb b/docs/source/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb
index 7b91e46d88..3887a40197 100644
--- a/docs/source/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb
+++ b/docs/source/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb
@@ -134,9 +134,9 @@
     "\n",
     "The first step is to encode our domain knowledge into a causal model, often represented as a graph. The final outcome of a causal inference analysis depends largely on the input assumptions, so this step is quite important. To estimate the causal effect, most common problems involve specifying two types of variables: \n",
     "\n",
-    "1. **Confounders**: These are variables that cause both the action and the outcome. As a result, any observed correlation between the action and the outcome may simply be due to the confounder variables, and not due to any causal relationship from the action to the outcome. \n",
+    "1. **Confounders (common_causes)**: These are variables that cause both the action and the outcome. As a result, any observed correlation between the action and the outcome may simply be due to the confounder variables, and not due to any causal relationship from the action to the outcome. \n",
     "\n",
-    "2. **Instrumental Variables**: These are special variables that cause the action, but do not directly affect the outcome. In addition, they are not affected by any variable that affects the outcome. Instrumental variables can help reduce bias, if used in the correct way. "
+    "2. **Instrumental Variables (instruments)**: These are special variables that cause the action, but do not directly affect the outcome. In addition, they are not affected by any variable that affects the outcome. Instrumental variables can help reduce bias, if used in the correct way. "
    ]
   },
   {
@@ -151,7 +151,7 @@
     "    treatment=data[\"treatment_name\"],\n",
     "    outcome=data[\"outcome_name\"],\n",
     "    common_causes=data[\"common_causes_names\"],\n",
-    "    intrumental_variables=data[\"instrument_names\"])"
+    "    instruments=data[\"instrument_names\"])"
    ]
   },
   {