def init_data():
data = linear_dataset(beta=10,
num_common_causes=4,
num_instruments=2,
num_effect_modifiers=2,
num_treatments=1,
num_samples=1000,
treatment_is_binary=True)
return data
def test_iv_estimators(self):
keras = pytest.importorskip("keras")
# Setup data
data = datasets.linear_dataset(10,
num_common_causes=4,
num_samples=10000,
num_instruments=2,
num_effect_modifiers=2,
num_treatments=1,
treatment_is_binary=False)
df = data['df']
model = CausalModel(data=data["df"],
treatment=data["treatment_name"],
outcome=data["outcome_name"],
effect_modifiers=data["effect_modifier_names"],
graph=data["gml_graph"])
identified_estimand = model.identify_effect(
proceed_when_unidentifiable=True)
# Test DeepIV
dims_zx = len(model._instruments) + len(model._effect_modifiers)
dims_tx = len(model._treatment) + len(model._effect_modifiers)
treatment_model = keras.Sequential([
keras.layers.Dense(
128, activation='relu',
input_shape=(dims_zx, )), # sum of dims of Z and X
keras.layers.Dropout(0.17),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dropout(0.17),
keras.layers.Dense(32, activation='relu'),
keras.layers.Dropout(0.17)
])
response_model = keras.Sequential([
keras.layers.Dense(
128, activation='relu',
input_shape=(dims_tx, )), # sum of dims of T and X
keras.layers.Dropout(0.17),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dropout(0.17),
keras.layers.Dense(32, activation='relu'),
keras.layers.Dropout(0.17),
keras.layers.Dense(1)
])
deepiv_estimate = model.estimate_effect(
identified_estimand,
method_name="iv.econml.deepiv.DeepIVEstimator",
target_units=lambda df: df["X0"] > -1,
confidence_intervals=False,
method_params={
"init_params": {
'n_components':
10, # Number of gaussians in the mixture density networks
# Treatment model,
'm':
lambda z, x: treatment_model(
keras.layers.concatenate([z, x])),
# Response model
"h":
lambda t, x: response_model(
keras.layers.concatenate([t, x])),
'n_samples':
1, # Number of samples used to estimate the response
'first_stage_options': {
'epochs': 25
},
'second_stage_options': {
'epochs': 25
}
},
"fit_params": {}
})
# Test IntentToTreatDRIV
data = datasets.linear_dataset(10,
num_common_causes=4,
num_samples=10000,
num_instruments=1,
num_effect_modifiers=2,
num_treatments=1,
treatment_is_binary=True,
num_discrete_instruments=1)
df = data['df']
model = CausalModel(data=data["df"],
treatment=data["treatment_name"],
outcome=data["outcome_name"],
effect_modifiers=data["effect_modifier_names"],
graph=data["gml_graph"])
identified_estimand = model.identify_effect(
proceed_when_unidentifiable=True)
driv_estimate = model.estimate_effect(
identified_estimand,
method_name="iv.econml.ortho_iv.LinearIntentToTreatDRIV",
target_units=lambda df: df["X0"] > 1,
confidence_intervals=False,
method_params={
"init_params": {
'model_T_XZ': GradientBoostingClassifier(),
'model_Y_X': GradientBoostingRegressor(),
'flexible_model_effect': GradientBoostingRegressor(),
'featurizer': PolynomialFeatures(degree=1,
include_bias=False)
},
"fit_params": {}
})
def test_backdoor_estimators(self):
# Setup data
data = datasets.linear_dataset(10,
num_common_causes=4,
num_samples=10000,
num_instruments=2,
num_effect_modifiers=2,
num_treatments=1,
treatment_is_binary=False)
df = data['df']
model = CausalModel(data=data["df"],
treatment=data["treatment_name"],
outcome=data["outcome_name"],
effect_modifiers=data["effect_modifier_names"],
graph=data["gml_graph"])
identified_estimand = model.identify_effect(
proceed_when_unidentifiable=True)
# Test LinearDML
dml_estimate = model.estimate_effect(
identified_estimand,
method_name="backdoor.econml.dml.LinearDML",
control_value=0,
treatment_value=1,
target_units=lambda df: df["X0"] > 1, # condition used for CATE
method_params={
"init_params": {
'model_y': GradientBoostingRegressor(),
'model_t': GradientBoostingRegressor(),
'featurizer': PolynomialFeatures(degree=1,
include_bias=True)
},
"fit_params": {}
})
# Test ContinuousTreatmentOrthoForest
orthoforest_estimate = model.estimate_effect(
identified_estimand,
method_name=
"backdoor.econml.ortho_forest.ContinuousTreatmentOrthoForest",
target_units=lambda df: df["X0"] > 2,
method_params={
"init_params": {
'n_trees': 10
},
"fit_params": {}
})
# Test LinearDRLearner
data_binary = datasets.linear_dataset(10,
num_common_causes=4,
num_samples=10000,
num_instruments=2,
num_effect_modifiers=2,
treatment_is_binary=True,
outcome_is_binary=True)
model_binary = CausalModel(
data=data_binary["df"],
treatment=data_binary["treatment_name"],
outcome=data_binary["outcome_name"],
effect_modifiers=data["effect_modifier_names"],
graph=data_binary["gml_graph"])
identified_estimand_binary = model_binary.identify_effect(
proceed_when_unidentifiable=True)
drlearner_estimate = model_binary.estimate_effect(
identified_estimand_binary,
method_name="backdoor.econml.drlearner.LinearDRLearner",
target_units=lambda df: df["X0"] > 1,
confidence_intervals=False,
method_params={
"init_params": {
'model_propensity':
LogisticRegressionCV(cv=3,
solver='lbfgs',
multi_class='auto')
},
"fit_params": {}
})
def test_iv_estimators(self):
# Setup data
data = datasets.linear_dataset(10,
num_common_causes=4,
num_samples=10000,
num_instruments=2,
num_effect_modifiers=2,
num_treatments=1,
treatment_is_binary=False)
df = data['df']
model = CausalModel(data=data["df"],
treatment=data["treatment_name"],
outcome=data["outcome_name"],
effect_modifiers=data["effect_modifier_names"],
graph=data["gml_graph"])
identified_estimand = model.identify_effect(
proceed_when_unidentifiable=True)
# Test DeepIV
dims_zx = len(model._instruments) + len(model._effect_modifiers)
dims_tx = len(model._treatment) + len(model._effect_modifiers)
treatment_model = keras.Sequential([
keras.layers.Dense(
128, activation='relu',
input_shape=(dims_zx, )), # sum of dims of Z and X
keras.layers.Dropout(0.17),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dropout(0.17),
keras.layers.Dense(32, activation='relu'),
keras.layers.Dropout(0.17)
])
response_model = keras.Sequential([
keras.layers.Dense(
128, activation='relu',
input_shape=(dims_tx, )), # sum of dims of T and X
keras.layers.Dropout(0.17),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dropout(0.17),
keras.layers.Dense(32, activation='relu'),
keras.layers.Dropout(0.17),
keras.layers.Dense(1)
])
deepiv_estimate = model.estimate_effect(
identified_estimand,
method_name="iv.econml.deepiv.DeepIVEstimator",
target_units=lambda df: df["X0"] > -1,
confidence_intervals=False,
method_params={
"init_params": {
'n_components':
10, # Number of gaussians in the mixture density networks
'm':
lambda z, x: treatment_model(
keras.layers.concatenate([z, x])), # Treatment model,
"h":
lambda t, x: response_model(
keras.layers.concatenate([t, x])), # Response model
'n_samples':
1, # Number of samples used to estimate the response
'first_stage_options': {
'epochs': 25
},
'second_stage_options': {
'epochs': 25
}
},
"fit_params": {}
})
class DoWhyExample:
data_old = ds.linear_dataset(beta=10,
num_common_causes=5,
num_instruments=5,
num_samples=10000,
treatment_is_binary=True)
gml_graph = ('graph[directed 1'
'node[ id "TOJ" label "TOJ"]'
'node[ id "IntCur" label "IntCur"]'
'node[ id "U" label "Unobserved Confounders"]'
'edge[source "TOJ" target "IntCur"]'
'edge[source "U" target "TOJ"]'
'edge[source "U" target "IntCur"]')
gml_graph = add_node(gml_graph, "YeshivaAdults", "IntCur")
gml_graph = add_node(gml_graph, "Sex", "IntCur")
gml_graph = add_node(gml_graph, "Age", "IntCur")
gml_graph = connect_node(gml_graph, "Age", "TOJ")
gml_graph = connect_node(gml_graph, "Age", "YeshivaAdults")
gml_graph = connect_node(gml_graph, "Sex", "YeshivaAdults")
gml_graph = connect_node(gml_graph, "TOJ", "YeshivaAdults")
gml_graph = gml_graph + ']'
# table
# ID Age Sex TOJ (Orthodox)? (Treatment?) Yeshiva? Intell. Curios? (Outcome)
data = pd.DataFrame(
np.array([[30.0, 1.0, 1.0, 1.0, 0.0], [40.0, 1.0, 0.0, 0.0, 1.0]]),
columns=['Age', 'Sex', 'TOJ', 'YeshivaAdults', 'IntCur'])
#
t_model = None
t_identify = None
t_estimate = None
def model(self, force_again=False):
if self.t_model is None or force_again:
self.t_model = CausalModel(data=self.data,
treatment='TOJ',
outcome='IntCur',
graph=self.gml_graph)
# CausalModel(data=self.data["df"],
# treatment=self.data["treatment_name"],
# outcome=self.data["outcome_name"],
# graph=self.data["gml_graph"])
return self.t_model
def identify(self, force_again=False):
if self.t_identify is None or force_again:
if self.t_model is None or force_again:
self.model(force_again=force_again)
self.t_identify = self.t_model.identify_effect()
return self.t_identify
def estimate(self,
method_name="backdoor.propensity_score_matching",
force_again=False):
if self.t_estimate is None or force_again:
self.t_estimate = self.t_model.estimate_effect(
self.identify(force_again), method_name)
return self.t_estimate
def refute(self, method_name="random_common_cause", force_again=False):
return self.model(force_again=force_again).refute_estimate(
self.identify(force_again),
self.estimate(force_again=force_again),
method_name=method_name)