def test_BaseTLearner(generate_regression_data):
y, X, treatment, tau, b, e = generate_regression_data()
learner = BaseTLearner(learner=XGBRegressor())
# check the accuracy of the ATE estimation
ate_p, lb, ub = learner.estimate_ate(X=X, treatment=treatment, y=y)
assert (ate_p >= lb) and (ate_p <= ub)
assert ape(tau.mean(), ate_p) < ERROR_THRESHOLD
# check the accuracy of the CATE estimation with the bootstrap CI
cate_p, _, _ = learner.fit_predict(X=X,
treatment=treatment,
y=y,
return_ci=True,
n_bootstraps=10)
auuc_metrics = pd.DataFrame({
'cate_p': cate_p.flatten(),
'W': treatment,
'y': y,
'treatment_effect_col': tau
})
cumgain = get_cumgain(auuc_metrics,
outcome_col='y',
treatment_col='W',
treatment_effect_col='tau')
# Check if the cumulative gain when using the model's prediction is
# higher than it would be under random targeting
assert cumgain['cate_p'].sum() > cumgain['Random'].sum()
def test_BaseTLearner(generate_regression_data):
y, X, treatment, tau, b, e = generate_regression_data()
learner = BaseTLearner(learner=XGBRegressor())
# check the accuracy of the ATE estimation
ate_p, lb, ub = learner.estimate_ate(X=X, treatment=treatment, y=y)
assert (ate_p >= lb) and (ate_p <= ub)
assert ape(tau.mean(), ate_p) < ERROR_THRESHOLD
# check the accuracy of the CATE estimation with the bootstrap CI
cate_p, _, _ = learner.fit_predict(X=X, treatment=treatment, y=y, return_ci=True, n_bootstraps=10)
assert gini(tau, cate_p.flatten()) > .5
def test_pandas_input(generate_regression_data):
y, X, treatment, tau, b, e = generate_regression_data()
# convert to pandas types
y = pd.Series(y)
X = pd.DataFrame(X)
treatment = pd.Series(treatment)
try:
learner = BaseSLearner(learner=LinearRegression())
ate_p, lb, ub = learner.estimate_ate(X=X, treatment=treatment, y=y, return_ci=True)
except AttributeError:
assert False
try:
learner = BaseTLearner(learner=LinearRegression())
ate_p, lb, ub = learner.estimate_ate(X=X, treatment=treatment, y=y)
except AttributeError:
assert False
try:
learner = BaseXLearner(learner=LinearRegression())
ate_p, lb, ub = learner.estimate_ate(X=X, treatment=treatment, y=y, p=e)
except AttributeError:
assert False
try:
learner = BaseRLearner(learner=LinearRegression())
ate_p, lb, ub = learner.estimate_ate(X=X, treatment=treatment, y=y, p=e)
except AttributeError:
assert False
try:
learner = TMLELearner(learner=LinearRegression())
ate_p, lb, ub = learner.estimate_ate(X=X, treatment=treatment, y=y, p=e)
except AttributeError:
assert False
def test_BaseTLearner(generate_regression_data):
y, X, treatment, tau, b, e = generate_regression_data()
learner = BaseTLearner(learner=XGBRegressor())
# check the accuracy of the ATE estimation
ate_p, lb, ub = learner.estimate_ate(X=X, treatment=treatment, y=y)
assert (ate_p >= lb) and (ate_p <= ub)
assert ape(tau.mean(), ate_p) < ERROR_THRESHOLD
# check pre-train model
ate_p_pt, lb_pt, ub_pt = learner.estimate_ate(X=X,
treatment=treatment,
y=y,
pretrain=True)
assert (ate_p_pt == ate_p) and (lb_pt == lb) and (ub_pt == ub)
# check the accuracy of the CATE estimation with the bootstrap CI
cate_p, _, _ = learner.fit_predict(X=X,
treatment=treatment,
y=y,
return_ci=True,
n_bootstraps=10)
auuc_metrics = pd.DataFrame({
"cate_p": cate_p.flatten(),
"W": treatment,
"y": y,
"treatment_effect_col": tau,
})
cumgain = get_cumgain(auuc_metrics,
outcome_col="y",
treatment_col="W",
treatment_effect_col="tau")
# Check if the cumulative gain when using the model's prediction is
# higher than it would be under random targeting
assert cumgain["cate_p"].sum() > cumgain["Random"].sum()
# test of using control_learner and treatment_learner
learner = BaseTLearner(
learner=XGBRegressor(),
control_learner=RandomForestRegressor(),
treatment_learner=RandomForestRegressor(),
)
# check the accuracy of the ATE estimation
ate_p, lb, ub = learner.estimate_ate(X=X, treatment=treatment, y=y)
assert (ate_p >= lb) and (ate_p <= ub)
assert ape(tau.mean(), ate_p) < ERROR_THRESHOLD