Added more admonitions

docs/src/guide/doublemachinelearning.md

@@ -93,13 +93,23 @@ consistency assumption and the minimum and maximu probabilities of treatment for
 positivity assumption with the num_treatments, min, and max keyword arguments.
 
 
-For a thorough review of casual inference assumptions see:
-    Hernan, Miguel A., and James M. Robins. Causal inference what if. Boca Raton: Taylor and 
-    Francis, 2024. 
+!!! note
+    For a thorough review of casual inference assumptions see:
+
+        Hernan, Miguel A., and James M. Robins. Causal inference what if. Boca Raton: Taylor and 
+        Francis, 2024. 
+
+!!! note
+    For more information on the E-value test see:
+
+        VanderWeele, Tyler J., and Peng Ding. "Sensitivity analysis in observational research: 
+        introducing the E-value." Annals of internal medicine 167, no. 4 (2017): 268-274.
+
+!!! danger
+    Obtaining correct estimates is dependent on meeting the assumptions for double machine 
+    learning. If the assumptions are not met then any estimates may be biased and lead to 
+    incorrect conclusions.
 
-For more information on the E-value test see:
-    VanderWeele, Tyler J., and Peng Ding. "Sensitivity analysis in observational research: 
-    introducing the E-value." Annals of internal medicine 167, no. 4 (2017): 268-274.
 ```julia
 validate(g_computer)
 ```

docs/src/guide/gcomputation.md

@@ -88,14 +88,23 @@ One can also specify the maxium number of possible treatments to consider for th
 consistency assumption and the minimum and maximu probabilities of treatment for the 
 positivity assumption with the num_treatments, min, and max keyword arguments.
 
+!!! note
+    For a thorough review of casual inference assumptions see:
 
-For a thorough review of casual inference assumptions see:
-    Hernan, Miguel A., and James M. Robins. Causal inference what if. Boca Raton: Taylor and 
-    Francis, 2024. 
+        Hernan, Miguel A., and James M. Robins. Causal inference what if. Boca Raton: Taylor and 
+        Francis, 2024. 
+
+!!! note
+    For more information on the E-value test see:
+
+        VanderWeele, Tyler J., and Peng Ding. "Sensitivity analysis in observational research: 
+        introducing the E-value." Annals of internal medicine 167, no. 4 (2017): 268-274.
+
+!!! danger
+    Obtaining correct estimates is dependent on meeting the assumptions for G-computation. 
+    If the assumptions are not met then any estimates may be biased and lead to incorrect 
+    conclusions.
 
-For more information on the E-value test see:
-    VanderWeele, Tyler J., and Peng Ding. "Sensitivity analysis in observational research: 
-    introducing the E-value." Annals of internal medicine 167, no. 4 (2017): 268-274.
 ```julia
 validate(g_computer)
 ```

docs/src/guide/its.md

@@ -86,6 +86,12 @@ tests in one line of code.
 One can also specify the number of simulated confounders to generate to test the sensitivity 
 of the model to confounding and the minimum and maximum proportion of data to use in the 
 Wald supremum test by including the n, low, and high keyword arguments.
+
+!!! danger
+    Obtaining correct estimates is dependent on meeting the assumptions for interrupted time 
+    series estimation. If the assumptions are not met then any estimates may be biased and 
+    lead to incorrect conclusions.
+
 ```julia
 validate(its)
 ```

docs/src/guide/metalearners.md

@@ -11,7 +11,7 @@ they can only handle binary treatments. On the other hand, R-learners can handle
 categorical, count, or continuous treatments but only supports continuous outcomes.
 
 !!! note
-    For a deeper dive on S-learning, T-learning, and R-learning see:
+    For a deeper dive on S-learning, T-learning, and X-learning see:
 
         Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. "Metalearners for 
         estimating heterogeneous treatment effects using machine learning." Proceedings of the 
@@ -97,13 +97,23 @@ One can also specify the maxium number of possible treatments to consider for th
 consistency assumption and the minimum and maximu probabilities of treatment for the 
 positivity assumption with the num_treatments, min, and max keyword arguments.
 
-For a thorough review of casual inference assumptions see:
-    Hernan, Miguel A., and James M. Robins. Causal inference what if. Boca Raton: Taylor and 
-    Francis, 2024. 
+!!! note
+    For a thorough review of casual inference assumptions see:
+
+        Hernan, Miguel A., and James M. Robins. Causal inference what if. Boca Raton: Taylor and 
+        Francis, 2024. 
+
+!!! note
+    For more information on the E-value test see:
+
+        VanderWeele, Tyler J., and Peng Ding. "Sensitivity analysis in observational research: 
+        introducing the E-value." Annals of internal medicine 167, no. 4 (2017): 268-274.
+
+!!! danger
+    Obtaining correct estimates is dependent on meeting the assumptions for interrupted time 
+    series estimation. If the assumptions are not met then any estimates may be biased and 
+    lead to incorrect conclusions.
 
-For more information on the E-value test see:
-    VanderWeele, Tyler J., and Peng Ding. "Sensitivity analysis in observational research: 
-    introducing the E-value." Annals of internal medicine 167, no. 4 (2017): 268-274.
 ```julia
 validate(s_learner)
 validate(t_learner)