def test_graph_input4(self, beta, num_instruments, num_samples, num_treatments):
num_common_causes = 5
data = dowhy.datasets.linear_dataset(beta=beta,
num_common_causes=num_common_causes,
num_instruments=num_instruments,
num_samples=num_samples,
num_treatments = num_treatments,
treatment_is_binary=True)
model = CausalModel(
data=data['df'],
treatment=data["treatment_name"],
outcome=data["outcome_name"],
graph=data["gml_graph"],
proceed_when_unidentifiable=True,
test_significance=None
)
# removing two common causes
gml_str = "tests/sample_dag.txt"
print(gml_str)
model = CausalModel(
data=data['df'],
treatment=data["treatment_name"],
outcome=data["outcome_name"],
graph=gml_str,
proceed_when_unidentifiable=True,
test_significance=None,
missing_nodes_as_confounders=True
)
common_causes = model.get_common_causes()
assert all(node_name in common_causes for node_name in ["X1", "X2"])
all_nodes = model._graph.get_all_nodes(include_unobserved=True)
assert all(node_name in all_nodes for node_name in ["Unobserved Confounders", "X0", "X1", "X2", "Z0", "v0", "y"])
all_nodes = model._graph.get_all_nodes(include_unobserved=False)
assert "Unobserved Confounders" not in all_nodes
def test_graph_input(self, beta, num_instruments, num_samples, num_treatments):
num_common_causes = 5
data = dowhy.datasets.linear_dataset(beta=beta,
num_common_causes=num_common_causes,
num_instruments=num_instruments,
num_samples=num_samples,
num_treatments = num_treatments,
treatment_is_binary=True)
model = CausalModel(
data=data['df'],
treatment=data["treatment_name"],
outcome=data["outcome_name"],
graph=data["gml_graph"],
proceed_when_unidentifiable=True,
test_significance=None
)
# removing two common causes
gml_str = 'graph[directed 1 node[ id "{0}" label "{0}"]node[ id "{1}" label "{1}"]node[ id "Unobserved Confounders" label "Unobserved Confounders"]edge[source "{0}" target "{1}"]edge[source "Unobserved Confounders" target "{0}"]edge[source "Unobserved Confounders" target "{1}"]node[ id "X0" label "X0"] edge[ source "X0" target "{0}"] node[ id "X1" label "X1"] edge[ source "X1" target "{0}"] node[ id "X2" label "X2"] edge[ source "X2" target "{0}"] edge[ source "X0" target "{1}"] edge[ source "X1" target "{1}"] edge[ source "X2" target "{1}"] node[ id "Z0" label "Z0"] edge[ source "Z0" target "{0}"]]'.format(data["treatment_name"][0], data["outcome_name"])
print(gml_str)
model = CausalModel(
data=data['df'],
treatment=data["treatment_name"],
outcome=data["outcome_name"],
graph=gml_str,
proceed_when_unidentifiable=True,
test_significance=None,
missing_nodes_as_confounders=True
)
common_causes = model.get_common_causes()
assert all(node_name in common_causes for node_name in ["X1", "X2"])
def test_graph_input3(self, beta, num_instruments, num_samples, num_treatments):
num_common_causes = 5
data = dowhy.datasets.linear_dataset(beta=beta,
num_common_causes=num_common_causes,
num_instruments=num_instruments,
num_samples=num_samples,
num_treatments = num_treatments,
treatment_is_binary=True)
model = CausalModel(
data=data['df'],
treatment=data["treatment_name"],
outcome=data["outcome_name"],
graph=data["gml_graph"],
proceed_when_unidentifiable=True,
test_significance=None
)
# removing two common causes
gml_str = """dag {
"Unobserved Confounders" [pos="0.491,-1.056"]
X0 [pos="-2.109,0.057"]
X1 [adjusted, pos="-0.453,-1.562"]
X2 [pos="-2.268,-1.210"]
Z0 [pos="-1.918,-1.735"]
v0 [latent, pos="-1.525,-1.293"]
y [outcome, pos="-1.164,-0.116"]
"Unobserved Confounders" -> v0
"Unobserved Confounders" -> y
X0 -> v0
X0 -> y
X1 -> v0
X1 -> y
X2 -> v0
X2 -> y
Z0 -> v0
v0 -> y
}
"""
print(gml_str)
model = CausalModel(
data=data['df'],
treatment=data["treatment_name"],
outcome=data["outcome_name"],
graph=gml_str,
proceed_when_unidentifiable=True,
test_significance=None,
missing_nodes_as_confounders=True
)
common_causes = model.get_common_causes()
assert all(node_name in common_causes for node_name in ["X1", "X2"])
all_nodes = model._graph.get_all_nodes(include_unobserved=True)
assert all(node_name in all_nodes for node_name in ["Unobserved Confounders", "X0", "X1", "X2", "Z0", "v0", "y"])
all_nodes = model._graph.get_all_nodes(include_unobserved=False)
assert "Unobserved Confounders" not in all_nodes