[DOC] fix typo (#1204)

* [DOC] fix typo

Signed-off-by: yogabonito <yogabonito@users.noreply.github.com>

* [DOC] small fix

The text first informs that PC, FCI, and GES will be considered, but instead of FCI the notebook shows LiNGAM.

Signed-off-by: yogabonito <yogabonito@users.noreply.github.com>

---------

Signed-off-by: yogabonito <yogabonito@users.noreply.github.com>

docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Causal Discovery example\n",
     "\n",
-    "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [causal-learn](https://github.com/py-why/causal-learn) repo. As we will see, causal discovery methods require appropriate assumptions for the correctness guarantees, adn thus there will be variance across results returned by different methods in practice. These methods, however, may be combined usefully with domain knowledge to construct the final causal graph."
+    "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [causal-learn](https://github.com/py-why/causal-learn) repo. As we will see, causal discovery methods require appropriate assumptions for the correctness guarantees, and thus there will be variance across results returned by different methods in practice. These methods, however, may be combined usefully with domain knowledge to construct the final causal graph."
    ]
   },
   {
@@ -96,7 +96,7 @@
    "source": [
     "# Causal Discovery with causal-learn\n",
     "\n",
-    "We use the causal-learn library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here: PC, FCI and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Causal-learn provides a comprehensive list of well-tested causal-discovery methods, and readers are welcome to explore.\n",
+    "We use the causal-learn library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here: PC, GES, and LiNGAM. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Causal-learn provides a comprehensive list of well-tested causal-discovery methods, and readers are welcome to explore.\n",
     "\n",
     "The documentation for the methods used are as follows:\n",
     "- PC [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Constraint-based%20causal%20discovery%20methods/PC.html)\n",