diff --git a/causalpy/pymc_experiments.py b/causalpy/pymc_experiments.py
index fb85b756..de84774d 100644
--- a/causalpy/pymc_experiments.py
+++ b/causalpy/pymc_experiments.py
@@ -302,17 +302,19 @@ def plot(self):
         fig, ax = plt.subplots()
 
         # Plot raw data
-        sns.lineplot(
-            self.data,
-            x=self.time_variable_name,
-            y=self.outcome_variable_name,
-            hue=self.group_variable_name,
-            units="unit",
-            estimator=None,
-            alpha=0.25,
-            ax=ax,
-        )
+        # NOTE: This will not work when there is just ONE unit in each group
+        # sns.lineplot(
+        #     self.data,
+        #     x=self.time_variable_name,
+        #     y=self.outcome_variable_name,
+        #     hue=self.group_variable_name,
+        #     # units="unit",
+        #     estimator=None,
+        #     alpha=0.25,
+        #     ax=ax,
+        # )
         # Plot model fit to control group
+        # NOTE: This will not work when there is just ONE unit in each group
         parts = ax.violinplot(
             az.extract(
                 self.y_pred_control, group="posterior_predictive", var_names="mu"
@@ -328,6 +330,7 @@ def plot(self):
             pc.set_alpha(0.5)
 
         # Plot model fit to treatment group
+        # NOTE: This will not work when there is just ONE unit in each group
         parts = ax.violinplot(
             az.extract(
                 self.y_pred_treatment, group="posterior_predictive", var_names="mu"
@@ -337,18 +340,19 @@ def plot(self):
             showmedians=False,
             widths=0.2,
         )
-        # Plot counterfactual - post-test for treatment group IF no treatment had occurred.
-        parts = ax.violinplot(
-            az.extract(
-                self.y_pred_counterfactual,
-                group="posterior_predictive",
-                var_names="mu",
-            ).values.T,
-            positions=self.x_pred_counterfactual[self.time_variable_name].values,
-            showmeans=False,
-            showmedians=False,
-            widths=0.2,
-        )
+        # # Plot counterfactual - post-test for treatment group IF no treatment had occurred.
+        # # NOTE: This will not work when there is just ONE unit in each group
+        # parts = ax.violinplot(
+        #     az.extract(
+        #         self.y_pred_counterfactual,
+        #         group="posterior_predictive",
+        #         var_names="mu",
+        #     ).values.T,
+        #     positions=self.x_pred_counterfactual[self.time_variable_name].values,
+        #     showmeans=False,
+        #     showmedians=False,
+        #     widths=0.2,
+        # )
         # arrow to label the causal impact
         y_pred_treatment = (
             self.y_pred_treatment["posterior_predictive"]
@@ -378,9 +382,9 @@ def plot(self):
         )
         # formatting
         ax.set(
-            xlim=[-0.15, 1.25],
-            xticks=[0, 1],
-            xticklabels=["pre", "post"],
+            # xlim=[-0.15, 1.25],
+            xticks=self.x_pred_treatment[self.time_variable_name].values,
+            # xticklabels=["pre", "post"],
             title=self._causal_impact_summary_stat(),
         )
         ax.legend(fontsize=LEGEND_FONT_SIZE)