Merge branch 'main' into kebatt/bumpVersion

notebooks/CustomerScenarios/Case Study - Using EconML to evaluate the treatment effect of training program - Lalonde dataset.ipynb

@@ -941,7 +941,7 @@
     "Finally, we want to learn whether we could get heterogeneous treatment effect insight using EconML, which could better tell us what kind of people are more or less responsive to this training program. We will start with using the unbiased experimental dataset, and then we are also interested to learn whether the observational dataset could recover the same findings from the \n",
     "experiment.\n",
     "\n",
-    "We train a `CasualForestDML` to learn non-parametric heterogeneous treatment effect by fitting a Casual Forest as the final stage model. EconML also supports interpretability tools such as `SingleTreeCateInterpreter` to further segment the users with different responsiveness to the treatment.\n",
+    "We train a `CausalForestDML` to learn non-parametric heterogeneous treatment effect by fitting a Casual Forest as the final stage model. EconML also supports interpretability tools such as `SingleTreeCateInterpreter` to further segment the users with different responsiveness to the treatment.\n",
     "\n",
     "## Experimental Data"
    ]