diff --git a/pathpy/path_extraction/temporal_paths.py b/pathpy/path_extraction/temporal_paths.py
index 7a13cd8..ebf6c36 100644
--- a/pathpy/path_extraction/temporal_paths.py
+++ b/pathpy/path_extraction/temporal_paths.py
@@ -264,13 +264,11 @@ def generate_causal_tree(dag, root, node_map):
                 queue.append((w, depth+1))
                 # only consider nodes that have not already
                 # been added to this level
-                if not visited[node_map[w], depth+1]:
-                    # add edge to causal tree
-                    y = '{0}_{1}'.format(node_map[w], depth+1)
-                    edges.append((x, y))
-
-                    visited[node_map[w], depth+1] = True
-                    causal_mapping[y] = node_map[w]
+            y = '{0}_{1}'.format(node_map[w], depth+1)
+            if not visited[x, y]:
+                # add edge to causal tree
+                edges.append((x, y))
+                visited[x, y] = True
     
     # Adding all edges at once is more efficient!
     causal_tree.add_edges(edges)
diff --git a/tests/test_DAG.py b/tests/test_DAG.py
index 8ae10e9..175261f 100644
--- a/tests/test_DAG.py
+++ b/tests/test_DAG.py
@@ -240,6 +240,77 @@ def test_dag_from_temporal_network():
     assert sorted(dag.routes_to_node('a_5')) == sorted([['c_2', 'a_5'], ['a_1', 'b_4', 'a_5']])
 
 
+def test_generate_causal_tree_diamond():
+    """
+
+             (b,d,3)-(d,a,5)
+            /               \
+     (a,b,1)                 (a,e,6) 
+            \               /
+             (b,c,2)-(c,a,4)
+
+    ---------------------------------> t
+
+                 (d,2)
+                /     \          
+     (a,0)-(b,1)       (a,3)-(e,4) 
+                \     /
+                 (c,2)
+
+    -------------------------------> depth
+
+    """
+
+    tn = pp.TemporalNetwork()
+    tn.add_edge('a', 'b', 1)
+    tn.add_edge('b', 'c', 2)
+    tn.add_edge('b', 'd', 3)
+    tn.add_edge('c', 'a', 4)
+    tn.add_edge('d', 'a', 5)
+    tn.add_edge('a', 'e', 6)
+
+    delta = 10
+
+    dag, node_map = pp.DAG.from_temporal_network(tn, delta)
+    root = list(dag.roots)[0]
+
+    causal_tree, causal_mapping = pp.path_extraction.generate_causal_tree(dag, root, node_map)
+    assert set(causal_tree.edges.keys()) == set([('a_0', 'b_1'), ('b_1', 'd_2'), ('b_1', 'c_2'), ('d_2', 'a_3'), ('c_2', 'a_3'), ('a_3', 'e_4')])
+
+
+def test_generate_causal_tree_trapezium():
+    """
+
+             (b,c,2)-(c,b,3)
+            /               \
+     (a,b,1)-----------------(b,c,4)-(c,d,5)
+
+    ---------------------------------> t
+
+                       (b,3)-(c,4)-(d,5)
+                      /
+     (a,0)-(b,1)-(c,2)-(d,3)
+               
+    -------------------------------> depth
+
+    """
+
+    tn = pp.TemporalNetwork()
+    tn.add_edge('a', 'b', 1)
+    tn.add_edge('b', 'c', 2)
+    tn.add_edge('c', 'b', 3)
+    tn.add_edge('b', 'c', 4)
+    tn.add_edge('c', 'd', 5)
+
+    delta = 10
+
+    dag, node_map = pp.DAG.from_temporal_network(tn, delta)
+    root = list(dag.roots)[0]
+
+    causal_tree, causal_mapping = pp.path_extraction.generate_causal_tree(dag, root, node_map)
+    assert set(causal_tree.edges.keys()) == set([('a_0', 'b_1'), ('b_1', 'c_2'), ('c_2', 'd_3'), ('c_2', 'b_3'), ('b_3', 'c_4'), ('c_4', 'd_5')])
+
+
 @pytest.mark.networkx
 def test_strong_connected_components(random_network):
     from pathpy.classes.network import network_to_networkx