diff --git a/src/causalprog/algorithms/evaluate.py b/src/causalprog/algorithms/evaluate.py
index 7972cc0..72c7925 100644
--- a/src/causalprog/algorithms/evaluate.py
+++ b/src/causalprog/algorithms/evaluate.py
@@ -20,6 +20,12 @@ def evaluate_down_to(
         A dictionary of the values of all the nodes that are ancestors of the input node
 
     """
+    for label, value in values.items():
+        graph.get_node(label).assert_is_valid_value(value)
+
+    if outcome_node_label in values:
+        return {outcome_node_label: values[outcome_node_label]}
+
     computed_values: dict[str, float | npt.NDArray[float]] = {}
     nodes_to_evaluate = [
         n
@@ -46,6 +52,4 @@ def evaluate(
         The evaluation of the node
 
     """
-    if outcome_node_label in values:
-        return values[outcome_node_label]
     return evaluate_down_to(graph, outcome_node_label, values)[outcome_node_label]
diff --git a/src/causalprog/graph/node/base.py b/src/causalprog/graph/node/base.py
index 6a218e9..1e00647 100644
--- a/src/causalprog/graph/node/base.py
+++ b/src/causalprog/graph/node/base.py
@@ -168,3 +168,19 @@ def parents(self) -> list[str]:
             List of labels of parent nodes
 
         """
+
+    def is_valid_value(self, _value: float | npt.NDArray[float]) -> bool:
+        """Check if a value is valid for this node."""
+        return True
+
+    def assert_is_valid_value(self, value: float | npt.NDArray[float]) -> None:
+        """Check if a value is valid for this node."""
+        if not self.is_valid_value(value):
+            msg = (
+                f"Invalid value for {self.__class__.__name__}: "
+                f"{self.label} cannot be {value}"
+            )
+            raise ValueError(msg)
+        if self.shape != (value.shape if hasattr(value, "shape") else ()):
+            msg = f"Invalid value for node: {self.label}"
+            raise ValueError(msg)
diff --git a/src/causalprog/graph/node/random_variables.py b/src/causalprog/graph/node/random_variables.py
index be80f12..97669c9 100644
--- a/src/causalprog/graph/node/random_variables.py
+++ b/src/causalprog/graph/node/random_variables.py
@@ -1,7 +1,6 @@
 """Graph nodes representing random variables."""
 
 import typing
-from abc import abstractmethod
 
 import jax
 import numpy as np
@@ -69,22 +68,6 @@ def evaluate(
     def parents(self) -> list[str]:
         return self._parents
 
-    @abstractmethod
-    def is_valid_value(self, value: float | npt.NDArray[float]) -> bool:
-        """Check if a value is valid for this node."""
-
-    def assert_is_valid_value(self, value: float | npt.NDArray[float]) -> None:
-        """Check if a value is valid for this node."""
-        if not self.is_valid_value(value):
-            msg = (
-                f"Invalid value for {self.__class__.__name__}: "
-                f"{self.label} cannot be {value}"
-            )
-            raise ValueError(msg)
-        if self.shape != (value.shape if hasattr(value, "shape") else ()):
-            msg = f"Invalid value for node: {self.label}"
-            raise ValueError(msg)
-
 
 class ContinuousRandomVariableNode(RandomVariableNode):
     """A node containing a continuous random variable (RV)."""
@@ -93,10 +76,6 @@ class ContinuousRandomVariableNode(RandomVariableNode):
     def __repr__(self) -> str:
         return f'ContinuousRandomVariableNode(label="{self.label}")'
 
-    @override
-    def is_valid_value(self, value: float | npt.NDArray[float]) -> bool:
-        return True
-
     @override
     def copy(self) -> Node:
         return ContinuousRandomVariableNode(
diff --git a/tests/test_algorithms/test_evaluate.py b/tests/test_algorithms/test_evaluate.py
index 770897b..bf9d144 100644
--- a/tests/test_algorithms/test_evaluate.py
+++ b/tests/test_algorithms/test_evaluate.py
@@ -26,20 +26,20 @@ def evaluate_test_graph() -> Graph:
     [
         pytest.param(
             "l",
-            {"l": jnp.array([5.5]), "x": jnp.array([2.0, 0.0]), "c": 4.0},
-            {},
+            {"l": jnp.array([5.5]), "x": 2.0, "c": 4.0},
+            {"l": jnp.array([5.5])},
             id="DataNode evaluation w/ excess information provided",
         ),
         pytest.param(
             "z",
             {"z": jnp.array([2.0, 0.0])},
-            {},
+            {"z": jnp.array([2.0, 0.0])},
             id="DataNode evaluation",
         ),
         pytest.param(
             "c",
             {"c": 4.0},
-            {},
+            {"c": 4.0},
             id="DiscreteRVNode evaluation",
         ),
         pytest.param(
@@ -51,7 +51,7 @@ def evaluate_test_graph() -> Graph:
         pytest.param(
             "u_x",
             {"c": 4.0, "u_x": 1.0},
-            {},
+            {"u_x": 1.0},
             id="CtsRVNode evaluation, 'given that' overrides computed value",
         ),
         pytest.param(
@@ -103,7 +103,32 @@ def test_evaluate(
     computed_result_single = evaluate(
         evaluate_test_graph, outcome_node_label, initial_values
     )
-    if outcome_node_label in initial_values:
-        assert jnp.allclose(computed_result_single, initial_values[outcome_node_label])
-    else:
-        assert jnp.allclose(computed_result_single, computed_result[outcome_node_label])
+    assert jnp.allclose(computed_result_single, computed_result[outcome_node_label])
+
+
+@pytest.mark.parametrize(
+    ("outcome_node_label", "initial_values", "expected_error"),
+    [
+        pytest.param(
+            "c",
+            {"l": jnp.array([5.5]), "z": jnp.array([2.0, 0.0]), "c": 4.5},
+            ValueError("Invalid value for "),
+            id="Invalid value for discrete RV node",
+        ),
+        pytest.param(
+            "phi_x",
+            {"z": jnp.array([2.0, 0.0]), "x": 4.0},
+            ValueError("Missing input for node"),
+            id="Missing value for a parent",
+        ),
+    ],
+)
+def test_evaluate_error(
+    evaluate_test_graph: Graph,
+    outcome_node_label: str,
+    initial_values: dict[str, Array],
+    expected_error: BaseException,
+    raises_context,
+) -> None:
+    with raises_context(expected_error):
+        evaluate(evaluate_test_graph, outcome_node_label, initial_values)