From a177de87f8137b530357be9fac03804fa4d7a8fe Mon Sep 17 00:00:00 2001
From: Zhai Jinyuan <15210720098@fudan.edu.cn>
Date: Sat, 18 Jun 2022 01:07:51 +0800
Subject: [PATCH] fix typo (#636)

---
 ...treatment effect of training program - Lalonde dataset.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/notebooks/CustomerScenarios/Case Study - Using EconML to evaluate the treatment effect of training program - Lalonde dataset.ipynb b/notebooks/CustomerScenarios/Case Study - Using EconML to evaluate the treatment effect of training program - Lalonde dataset.ipynb
index af0628d1e..7b560bed6 100644
--- a/notebooks/CustomerScenarios/Case Study - Using EconML to evaluate the treatment effect of training program - Lalonde dataset.ipynb	
+++ b/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"
    ]