def test_missing_w_weights(self):
df = load_sample_data(False).drop(columns=['dead'])
df['age_sq'] = df['age0'] ** 2
df['age_cu'] = df['age0'] ** 3
df['cd4_sq'] = df['cd40'] ** 2
df['cd4_cu'] = df['cd40'] ** 3
df['weight'] = 2
snm = GEstimationSNM(df, exposure='art', outcome='cd4_wk45', weights='weight')
snm.exposure_model('male + age0 + age_sq + age_cu + cd40 + cd4_sq + cd4_cu + dvl0', print_results=False)
snm.missing_model('art + male + age0 + age_sq + age_cu + cd40 + cd4_sq + cd4_cu + dvl0',
stabilized=False, print_results=False)
snm.structural_nested_model('art')
snm.fit()
npt.assert_allclose(snm.psi, [244.379181], atol=1e-5)
snm = GEstimationSNM(df, exposure='art', outcome='cd4_wk45')
snm.exposure_model('male + age0 + age_sq + age_cu + cd40 + cd4_sq + cd4_cu + dvl0', print_results=False)
snm.missing_model('art + male + age0 + age_sq + age_cu + cd40 + cd4_sq + cd4_cu + dvl0',
stabilized=False, print_results=False)
snm.structural_nested_model('art')
snm.fit(solver='search')
npt.assert_allclose(snm.psi, [244.379181], atol=1e-5)
def test_weighted_model(self):
df = load_sample_data(False).drop(columns=['dead'])
df['age_sq'] = df['age0'] ** 2
df['age_cu'] = df['age0'] ** 3
df['cd4_sq'] = df['cd40'] ** 2
df['cd4_cu'] = df['cd40'] ** 3
ipmw = IPMW(df, missing_variable='cd4_wk45')
ipmw.regression_models('art + male + age0 + age_sq + age_cu + cd40 + cd4_sq + cd4_cu + dvl0',
print_results=False)
ipmw.fit()
df['ipcw'] = ipmw.Weight
snm = GEstimationSNM(df.dropna(), exposure='art', outcome='cd4_wk45', weights='ipcw')
snm.exposure_model('male + age0 + age_sq + age_cu + cd40 + cd4_sq + cd4_cu + dvl0', print_results=False)
snm.structural_nested_model('art')
snm.fit()
npt.assert_allclose(snm.psi, [244.379181], atol=1e-5)
snm = GEstimationSNM(df.dropna(), exposure='art', outcome='cd4_wk45', weights='ipcw')
snm.exposure_model('male + age0 + age_sq + age_cu + cd40 + cd4_sq + cd4_cu + dvl0', print_results=False)
snm.structural_nested_model('art')
snm.fit(solver='search')
npt.assert_allclose(snm.psi, [244.379181], atol=1e-5)
def test_invalid_solver(self, data_c):
snm = GEstimationSNM(data_c, exposure='art', outcome='cd4_wk45')
snm.exposure_model(
'male + age0 + age_sq + age_cu + cd40 + cd4_sq + cd4_cu + dvl0',
print_results=False)
snm.structural_nested_model('art')
with pytest.raises(ValueError):
snm.fit(solver='grid_search')
def test_solvermatch_1param_binary(self, data_b):
snm = GEstimationSNM(data_b, exposure='art', outcome='dead')
snm.exposure_model('male + age0 + age_sq + age_cu + cd40 + cd4_sq + cd4_cu + dvl0', print_results=False)
snm.structural_nested_model('art')
snm.fit(solver='closed')
closed = snm.psi
snm.fit(solver='search')
search = snm.psi
npt.assert_allclose(closed, search)
def test_solvermatch_2param_continuous(self, data_c):
snm = GEstimationSNM(data_c, exposure='art', outcome='cd4_wk45')
snm.exposure_model('male + age0 + age_sq + age_cu + cd40 + cd4_sq + cd4_cu + dvl0', print_results=False)
snm.structural_nested_model('art + art:male')
snm.fit(solver='closed')
closed = snm.psi
snm.fit(solver='search', starting_value=[200, -50])
search = snm.psi
npt.assert_allclose(closed, search)
def test_match_r_binary(self, data_b):
# Comparing to R's DTRreg
r_1param = [-0.0895]
r_2param = [-0.1760, 0.1084]
r_3param = [-0.2316, 0.1245, 0.000165]
r_5param = [0.3022, 0.1627, -0.1374, 0.0002, -0.0120]
# One-parameter SNM
snm = GEstimationSNM(data_b, exposure='art', outcome='dead')
snm.exposure_model(
'male + age0 + age_sq + age_cu + cd40 + cd4_sq + cd4_cu + dvl0',
print_results=False)
snm.structural_nested_model('art')
snm.fit(solver='closed')
closed = snm.psi
npt.assert_allclose(closed, r_1param, rtol=1e-3)
# Two-parameter SNM
snm.structural_nested_model('art + art:male')
snm.fit(solver='closed')
closed = snm.psi
npt.assert_allclose(closed, r_2param, rtol=1e-3)
# Three-parameter SNM
snm.structural_nested_model('art + art:male + art:cd40')
snm.fit(solver='closed')
closed = snm.psi
npt.assert_allclose(closed, r_3param, atol=1e-3)
# Five-parameter SNM
snm.structural_nested_model(
'art + art:male + art:dvl0 + art:cd40 + art:age0')
snm.fit(solver='closed')
closed = snm.psi
npt.assert_allclose(closed, r_5param, atol=1e-3)
def test_match_r_continuous(self, data_c):
# Comparing to R's DTRreg
r_1param = [251.4032]
r_2param = [289.4268, -47.8836]
r_3param = [436.0397, -81.0284, -0.4397]
r_5param = [974.4983, -50.3237, -285.2829, -0.3569, -8.5426]
# One-parameter SNM
snm = GEstimationSNM(data_c, exposure='art', outcome='cd4_wk45')
snm.exposure_model(
'male + age0 + age_sq + age_cu + cd40 + cd4_sq + cd4_cu + dvl0',
print_results=False)
snm.structural_nested_model('art')
snm.fit(solver='closed')
closed = snm.psi
npt.assert_allclose(closed, r_1param, rtol=1e-4)
# Two-parameter SNM
snm.structural_nested_model('art + art:male')
snm.fit(solver='closed')
closed = snm.psi
npt.assert_allclose(closed, r_2param, rtol=1e-4)
# Three-parameter SNM
snm.structural_nested_model('art + art:male + art:cd40')
snm.fit(solver='closed')
closed = snm.psi
npt.assert_allclose(closed, r_3param, rtol=1e-4)
# Five-parameter SNM
snm.structural_nested_model(
'art + art:male + art:dvl0 + art:cd40 + art:age0')
snm.fit(solver='closed')
closed = snm.psi
npt.assert_allclose(closed, r_5param, rtol=1e-4)
def test_error_when_no_models_specified3(self, data_c):
snm = GEstimationSNM(data_c, exposure='art', outcome='cd4_wk45')
snm.structural_nested_model('art')
with pytest.raises(ValueError):
snm.fit()
bound=0.01)
sctmle.outcome_model('male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0',
SuperLearner(candidates, labels, folds=10, loss_function="nloglik"))
sctmle.fit(n_partitions=3, random_state=201820)
sctmle.summary()
#############################
# G-estimation
from zepid.causal.snm import GEstimationSNM
snm = GEstimationSNM(df, exposure='art', outcome='dead')
# Specify treatment model
snm.exposure_model('male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0',
print_results=False)
# Specify structural nested model
snm.structural_nested_model('art')
# G-estimation
snm.fit()
snm.summary()
psi = snm.psi
print('Psi:', psi)
psi_results = []
for i in range(500):
with warnings.catch_warnings():
warnings.simplefilter(action='ignore', category=UserWarning)
dfs = df.sample(n=df.shape[0], replace=True)
snm = GEstimationSNM(dfs, exposure='art', outcome='dead')
snm.exposure_model('male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0',
print_results=False)