def test_elasticnet_propensity_model(generate_regression_data):
y, X, treatment, tau, b, e = generate_regression_data()
pm = ElasticNetPropensityModel(random_state=RANDOM_SEED)
ps = pm.fit_predict(X, treatment)
assert roc_auc_score(treatment, ps) > .5
def get_synthetic_preds(synthetic_data_func, n=1000, estimators={}):
"""Generate predictions for synthetic data using specified function (single simulation)
Args:
synthetic_data_func (function): synthetic data generation function
n (int, optional): number of samples
estimators (dict of object): dict of names and objects of treatment effect estimators
Returns:
(dict): dict of the actual and estimates of treatment effects
"""
y, X, w, tau, b, e = synthetic_data_func(n=n)
preds_dict = {}
preds_dict[KEY_ACTUAL] = tau
preds_dict[KEY_GENERATED_DATA] = {
"y": y,
"X": X,
"w": w,
"tau": tau,
"b": b,
"e": e,
}
# Predict p_hat because e would not be directly observed in real-life
p_model = ElasticNetPropensityModel()
p_hat = p_model.fit_predict(X, w)
if estimators:
for name, learner in estimators.items():
try:
preds_dict[name] = learner.fit_predict(X=X,
treatment=w,
y=y,
p=p_hat).flatten()
except TypeError:
preds_dict[name] = learner.fit_predict(X=X, treatment=w,
y=y).flatten()
else:
for base_learner, label_l in zip(
[BaseSRegressor, BaseTRegressor, BaseXRegressor, BaseRRegressor],
["S", "T", "X", "R"],
):
for model, label_m in zip([LinearRegression, XGBRegressor],
["LR", "XGB"]):
learner = base_learner(model())
model_name = "{} Learner ({})".format(label_l, label_m)
try:
preds_dict[model_name] = learner.fit_predict(
X=X, treatment=w, y=y, p=p_hat).flatten()
except TypeError:
preds_dict[model_name] = learner.fit_predict(
X=X, treatment=w, y=y).flatten()
learner = CausalTreeRegressor(random_state=RANDOM_SEED)
preds_dict["Causal Tree"] = learner.fit_predict(X=X, treatment=w,
y=y).flatten()
return preds_dict
def get_synthetic_preds(synthetic_data_func, n=1000, estimators={}):
"""Generate predictions for synthetic data using specified function (single simulation)
Args:
synthetic_data_func (function): synthetic data generation function
n (int, optional): number of samples
estimators (dict of object): dict of names and objects of treatment effect estimators
Returns:
(dict): dict of the actual and estimates of treatuement effects
"""
y, X, w, tau, b, e = synthetic_data_func(n=n)
preds_dict = {}
preds_dict[KEY_ACTUAL] = tau
preds_dict[KEY_GENERATED_DATA] = {
'y': y,
'X': X,
'w': w,
'tau': tau,
'b': b,
'e': e
}
# Predict p_hat because e would not be directly observed in real-life
p_model = ElasticNetPropensityModel()
p_hat = p_model.fit_predict(X, w)
if estimators:
for name, learner in estimators.items():
try:
preds_dict[name] = learner.fit_predict(X=X,
p=p_hat,
treatment=w,
y=y).flatten()
except TypeError:
preds_dict[name] = learner.fit_predict(X=X, treatment=w,
y=y).flatten()
else:
for base_learner, label_l in zip(
[BaseSLearner, BaseTLearner, BaseXLearner, BaseRLearner],
['S', 'T', 'X', 'R']):
for model, label_m in zip([LinearRegression, XGBRegressor],
['LR', 'XGB']):
learner = base_learner(model())
model_name = '{} Learner ({})'.format(label_l, label_m)
try:
preds_dict[model_name] = learner.fit_predict(
X=X, p=p_hat, treatment=w, y=y).flatten()
except TypeError:
preds_dict[model_name] = learner.fit_predict(
X=X, treatment=w, y=y).flatten()
learner = CausalTreeRegressor(random_state=RANDOM_SEED)
preds_dict['Causal Tree'] = learner.fit_predict(X=X, treatment=w,
y=y).flatten()
return preds_dict
def __init__(self,
outcome_learner=None,
effect_learner=None,
propensity_learner=ElasticNetPropensityModel(),
ate_alpha=.05,
control_name=0,
n_fold=5,
random_state=None):
"""Initialize an R-learner classifier.
Args:
outcome_learner: a model to estimate outcomes. Should be a classifier.
effect_learner: a model to estimate treatment effects. It needs to take `sample_weight` as an
input argument for `fit()`. Should be a regressor.
propensity_learner (optional): a model to estimate propensity scores. `ElasticNetPropensityModel()` will
be used by default.
ate_alpha (float, optional): the confidence level alpha of the ATE estimate
control_name (str or int, optional): name of control group
n_fold (int, optional): the number of cross validation folds for outcome_learner
random_state (int or RandomState, optional): a seed (int) or random number generator (RandomState)
"""
super().__init__(learner=None,
outcome_learner=outcome_learner,
effect_learner=effect_learner,
propensity_learner=propensity_learner,
ate_alpha=ate_alpha,
control_name=control_name,
n_fold=n_fold,
random_state=random_state)
if (outcome_learner is None) and (effect_learner is None):
raise ValueError(
"Either the outcome learner or the effect learner must be specified."
)
def __init__(
self,
learner=None,
outcome_learner=None,
effect_learner=None,
propensity_learner=ElasticNetPropensityModel(),
ate_alpha=0.05,
control_name=0,
n_fold=5,
random_state=None,
):
"""Initialize an R-learner regressor.
Args:
learner (optional): a model to estimate outcomes and treatment effects
outcome_learner (optional): a model to estimate outcomes
effect_learner (optional): a model to estimate treatment effects. It needs to take `sample_weight` as an
input argument for `fit()`
propensity_learner (optional): a model to estimate propensity scores. `ElasticNetPropensityModel()` will
be used by default.
ate_alpha (float, optional): the confidence level alpha of the ATE estimate
control_name (str or int, optional): name of control group
n_fold (int, optional): the number of cross validation folds for outcome_learner
random_state (int or RandomState, optional): a seed (int) or random number generator (RandomState)
"""
super().__init__(
learner=learner,
outcome_learner=outcome_learner,
effect_learner=effect_learner,
propensity_learner=propensity_learner,
ate_alpha=ate_alpha,
control_name=control_name,
n_fold=n_fold,
random_state=random_state,
)
def _generate_data():
if not generated:
y, X, treatment, tau, b, e = generate_regression_data()
features = ['x{}'.format(i) for i in range(X.shape[1])]
df = pd.DataFrame(X, columns=features)
df[TREATMENT_COL] = treatment
df_c = df.loc[treatment == 0]
df_t = df.loc[treatment == 1]
df = pd.concat([df_t, df_c, df_c], axis=0, ignore_index=True)
pm = ElasticNetPropensityModel(random_state=RANDOM_SEED)
ps = pm.fit_predict(df[features], df[TREATMENT_COL])
df[SCORE_COL] = ps
df[GROUP_COL] = np.random.randint(0, 2, size=df.shape[0])
return df, features
def __init__(
self,
learner=None,
outcome_learner=None,
effect_learner=None,
propensity_learner=ElasticNetPropensityModel(),
ate_alpha=0.05,
control_name=0,
n_fold=5,
random_state=None,
):
"""Initialize an R-learner.
Args:
learner (optional): a model to estimate outcomes and treatment effects
outcome_learner (optional): a model to estimate outcomes
effect_learner (optional): a model to estimate treatment effects. It needs to take `sample_weight` as an
input argument for `fit()`
propensity_learner (optional): a model to estimate propensity scores. `ElasticNetPropensityModel()` will
be used by default.
ate_alpha (float, optional): the confidence level alpha of the ATE estimate
control_name (str or int, optional): name of control group
n_fold (int, optional): the number of cross validation folds for outcome_learner
random_state (int or RandomState, optional): a seed (int) or random number generator (RandomState)
"""
assert (learner is not None) or (
(outcome_learner is not None) and (effect_learner is not None)
)
assert propensity_learner is not None
self.model_mu = (
outcome_learner if outcome_learner is not None else deepcopy(learner)
)
self.model_tau = (
effect_learner if effect_learner is not None else deepcopy(learner)
)
self.model_p = propensity_learner
self.ate_alpha = ate_alpha
self.control_name = control_name
self.random_state = random_state
self.cv = KFold(n_splits=n_fold, shuffle=True, random_state=random_state)
self.propensity = None
self.propensity_model = None
def get_synthetic_preds_holdout(synthetic_data_func,
n=1000,
valid_size=0.2,
estimators={}):
"""Generate predictions for synthetic data using specified function (single simulation) for train and holdout
Args:
synthetic_data_func (function): synthetic data generation function
n (int, optional): number of samples
valid_size(float,optional): validaiton/hold out data size
estimators (dict of object): dict of names and objects of treatment effect estimators
Returns:
(tuple): synthetic training and validation data dictionaries:
- preds_dict_train (dict): synthetic training data dictionary
- preds_dict_valid (dict): synthetic validation data dictionary
"""
y, X, w, tau, b, e = synthetic_data_func(n=n)
X_train, X_val, y_train, y_val, w_train, w_val, tau_train, tau_val, b_train, b_val, e_train, e_val = \
train_test_split(X, y, w, tau, b, e, test_size=valid_size, random_state=RANDOM_SEED, shuffle=True)
preds_dict_train = {}
preds_dict_valid = {}
preds_dict_train[KEY_ACTUAL] = tau_train
preds_dict_valid[KEY_ACTUAL] = tau_val
preds_dict_train['generated_data'] = {
'y': y_train,
'X': X_train,
'w': w_train,
'tau': tau_train,
'b': b_train,
'e': e_train
}
preds_dict_valid['generated_data'] = {
'y': y_val,
'X': X_val,
'w': w_val,
'tau': tau_val,
'b': b_val,
'e': e_val
}
# Predict p_hat because e would not be directly observed in real-life
p_model = ElasticNetPropensityModel()
p_hat_train = p_model.fit_predict(X_train, w_train)
p_hat_val = p_model.fit_predict(X_val, w_val)
for base_learner, label_l in zip(
[BaseSRegressor, BaseTRegressor, BaseXRegressor, BaseRRegressor],
['S', 'T', 'X', 'R']):
for model, label_m in zip([LinearRegression, XGBRegressor],
['LR', 'XGB']):
# RLearner will need to fit on the p_hat
if label_l != 'R':
learner = base_learner(model())
# fit the model on training data only
learner.fit(X=X_train, treatment=w_train, y=y_train)
try:
preds_dict_train['{} Learner ({})'.format(
label_l,
label_m)] = learner.predict(X=X_train,
p=p_hat_train).flatten()
preds_dict_valid['{} Learner ({})'.format(
label_l,
label_m)] = learner.predict(X=X_val,
p=p_hat_val).flatten()
except TypeError:
preds_dict_train['{} Learner ({})'.format(
label_l,
label_m)] = learner.predict(X=X_train,
treatment=w_train,
y=y_train).flatten()
preds_dict_valid['{} Learner ({})'.format(
label_l,
label_m)] = learner.predict(X=X_val,
treatment=w_val,
y=y_val).flatten()
else:
learner = base_learner(model())
learner.fit(X=X_train,
p=p_hat_train,
treatment=w_train,
y=y_train)
preds_dict_train['{} Learner ({})'.format(
label_l, label_m)] = learner.predict(X=X_train).flatten()
preds_dict_valid['{} Learner ({})'.format(
label_l, label_m)] = learner.predict(X=X_val).flatten()
return preds_dict_train, preds_dict_valid
