def df_iptw(df_c):
dfs = df_c.loc[df_c['S'] == 1].copy()
ipt = IPTW(dfs, treatment='A', outcome='Y')
ipt.treatment_model('L', stabilized=True, print_results=False)
dfs['iptw'] = ipt.iptw
return pd.concat([dfs, df_c.loc[df_c['S'] == 0]], ignore_index=True, sort=False)
def test_positivity_calculator(self, data):
ipt = IPTW(data, treatment='A', outcome='Y')
ipt.treatment_model(model_denominator='L', print_results=False)
ipt.marginal_structural_model('A')
ipt.fit()
ipt.positivity()
npt.assert_allclose(ipt._pos_avg, 1)
npt.assert_allclose(ipt._pos_sd, 0.456435, rtol=1e-5)
npt.assert_allclose(ipt._pos_min, 2 / 3)
npt.assert_allclose(ipt._pos_max, 2)
def test_match_r_stddiff(self):
# Simulated data for variable detection and standardized differences
df = pd.DataFrame()
df['y'] = [1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0]
df['treat'] = [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]
df['bin'] = [0, 1, 0, np.nan, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1]
df['con'] = [
0.1, 0.0, 1.0, 1.1, 2.2, 1.3, 0.1, 0.5, 0.9, 0.5, 0.3, 0.2, 0.7,
0.9, 1.4
]
df['dis'] = [0, 1, 3, 2, 1, 0, 0, 0, 0, 0, 1, 3, 2, 2, 1]
df['cat'] = [1, 2, 3, 1, 1, 2, 3, 1, 3, 2, 1, 2, 3, 2, 1]
ipt = IPTW(df, treatment='treat', outcome='y')
ipt.treatment_model(model_denominator='bin + con + dis + C(cat)',
print_results=False)
ipt.marginal_structural_model('treat')
ipt.fit()
smd = ipt.standardized_mean_differences()
npt.assert_allclose(np.array(smd['smd_u']),
np.array([0.342997, 0.0, 0.06668, -0.513553]),
rtol=1e-4) # for unweighted
# TODO need to find an R package or something that calculates weighted SMD
# currently compares to my own calculations
npt.assert_allclose(np.array(smd['smd_w']),
np.array([0.206072, -0.148404, 0.035683,
0.085775]),
rtol=1e-4) # for weighted
def test_iptw_w_censor2(self, cdata):
iptw = IPTW(cdata, treatment='art', outcome='cd4_wk45')
iptw.treatment_model('male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0', print_results=False)
iptw.missing_model('art + male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0',
print_results=False)
iptw.marginal_structural_model('art')
iptw.fit()
npt.assert_allclose(iptw.average_treatment_effect['ATE'][1], 205.11238, rtol=1e-5)
npt.assert_allclose((iptw.average_treatment_effect['95%LCL'][1], iptw.average_treatment_effect['95%UCL'][1]),
(96.88535, 313.33941), atol=1e-4, rtol=1e-4)
def test_iptw_w_censor(self, sdata):
iptw = IPTW(sdata, treatment='art', outcome='dead')
iptw.treatment_model('male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0', print_results=False)
iptw.missing_model('art + male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0',
print_results=False)
iptw.marginal_structural_model('art')
iptw.fit()
npt.assert_allclose(iptw.risk_difference['RD'][1], -0.08092, rtol=1e-5)
npt.assert_allclose((iptw.risk_difference['95%LCL'][1], iptw.risk_difference['95%UCL'][1]),
(-0.15641, -0.00543), atol=1e-4, rtol=1e-4)
def test_stabilized_weights(self, data):
ipt = IPTW(data, treatment='A', outcome='Y')
ipt.treatment_model(model_denominator='L', print_results=False)
ipt.marginal_structural_model('A')
ipt.fit()
npt.assert_allclose(
ipt.iptw,
[1.5, 1.5, 2 / 3, 2 / 3, 2 / 3, 3 / 4, 3 / 4, 3 / 4, 3 / 4, 2])
def test_positivity_calculator(self, data):
ipt = IPTW(data, treatment='A', stabilized=True)
ipt.regression_models(model_denominator='L', print_results=False)
ipt.fit()
ipt.positivity()
npt.assert_allclose(ipt._pos_avg, 1)
npt.assert_allclose(ipt._pos_sd, 0.456435, rtol=1e-5)
npt.assert_allclose(ipt._pos_min, 2 / 3)
npt.assert_allclose(ipt._pos_max, 2)
def test_match_r_stddiff(self):
# Simulated data for variable detection and standardized differences
df = pd.DataFrame()
df['treat'] = [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]
df['bin'] = [0, 1, 0, np.nan, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1]
df['con'] = [
0.1, 0.0, 1.0, 1.1, 2.2, 1.3, 0.1, 0.5, 0.9, 0.5, 0.3, 0.2, 0.7,
0.9, 1.4
]
df['dis'] = [0, 1, 3, 2, 1, 0, 0, 0, 0, 0, 1, 3, 2, 2, 1]
df['cat'] = [1, 2, 3, 1, 1, 2, 3, 1, 3, 2, 1, 2, 3, 2, 1]
ipt = IPTW(df, treatment='treat', stabilized=True)
ipt.regression_models('bin + con + dis + C(cat)')
ipt.fit()
smd = ipt.standardized_mean_differences()
npt.assert_allclose(np.array(smd['smd_u']),
np.array([0.342997, 0.0, 0.06668, -0.513553]),
rtol=1e-4) # for unweighted
# TODO need to find an R package or something that calculates weighted SMD
# currently compares to my own calculations
npt.assert_allclose(np.array(smd['smd_w']),
np.array([0.206072, -0.148404, 0.035752,
0.085844]),
rtol=1e-4) # for weighted
def test_standardized_differences(self, sdata):
ipt = IPTW(sdata, treatment='art', outcome='dead')
ipt.treatment_model(model_denominator='male + age0 + cd40 + dvl0', print_results=False)
ipt.marginal_structural_model('art')
ipt.fit()
smd = ipt.standardized_mean_differences()
npt.assert_allclose(np.array(smd['smd_u']),
np.array([-0.015684, 0.022311, -0.4867, -0.015729]),
rtol=1e-4) # for unweighted
# TODO find R package to test these weighted SMD's
npt.assert_allclose(np.array(smd['smd_w']),
np.array([-0.097789, -0.012395, -0.018591, 0.050719]),
rtol=1e-4) # for weighted
def test_match_sas_smr_u_stabilized(self, sdata):
sas_rd = -0.080048197
sas_rd_ci = -0.153567335, -0.006529058
model = 'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0'
ipt = IPTW(sdata, treatment='art', outcome='dead', standardize='unexposed')
ipt.treatment_model(model_denominator=model, print_results=False)
ipt.marginal_structural_model('art')
ipt.fit()
npt.assert_allclose(ipt.risk_difference['RD'][1], sas_rd, rtol=1e-5)
npt.assert_allclose((ipt.risk_difference['95%LCL'][1], ipt.risk_difference['95%UCL'][1]), sas_rd_ci, rtol=1e-4)
def test_match_sas_gbound3(self, sdata):
sas_rd = -0.045129870
sas_rd_ci = -0.128184899, 0.037925158
model = 'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0'
ipt = IPTW(sdata, treatment='art', outcome='dead')
ipt.treatment_model(model_denominator=model, print_results=False, bound=0.5)
ipt.marginal_structural_model('art')
ipt.fit()
npt.assert_allclose(ipt.risk_difference['RD'][1], sas_rd, rtol=1e-5)
npt.assert_allclose((ipt.risk_difference['95%LCL'][1], ipt.risk_difference['95%UCL'][1]), sas_rd_ci,
atol=1e-4, rtol=1e-4)
def test_match_sas_gbound2(self, sdata):
sas_rd = -0.050924398
sas_rd_ci = -0.133182382, 0.031333585
model = 'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0'
ipt = IPTW(sdata, treatment='art', outcome='dead')
ipt.treatment_model(model_denominator=model, print_results=False, bound=[0.2, 0.9])
ipt.marginal_structural_model('art')
ipt.fit()
npt.assert_allclose(ipt.risk_difference['RD'][1], sas_rd, rtol=1e-5)
npt.assert_allclose((ipt.risk_difference['95%LCL'][1], ipt.risk_difference['95%UCL'][1]), sas_rd_ci,
atol=1e-4, rtol=1e-4)
def test_match_sas_smr_e(self, sdata):
sas_w_sum = 158.288404
sas_rd = -0.090875986
sas_rd_ci = -0.180169444, -0.001582527
model = 'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0'
ipt = IPTW(sdata, treatment='art', outcome='dead', standardize='exposed')
ipt.treatment_model(model_denominator=model, stabilized=False, print_results=False)
ipt.marginal_structural_model('art')
ipt.fit()
npt.assert_allclose(np.sum(ipt.iptw), sas_w_sum, rtol=1e-4)
npt.assert_allclose(ipt.risk_difference['RD'][1], sas_rd, rtol=1e-5)
npt.assert_allclose((ipt.risk_difference['95%LCL'][1], ipt.risk_difference['95%UCL'][1]), sas_rd_ci, rtol=1e-4)
def test_match_sas_stabilized(self, sdata):
sas_w_sum = 546.0858419
sas_rd = -0.081519085
sas_rd_ci = -0.156199938, -0.006838231
model = 'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0'
ipt = IPTW(sdata, treatment='art', outcome='dead')
ipt.treatment_model(model_denominator=model, print_results=False)
ipt.marginal_structural_model('art')
ipt.fit()
npt.assert_allclose(np.sum(ipt.iptw), sas_w_sum, rtol=1e-4)
npt.assert_allclose(ipt.risk_difference['RD'][1], sas_rd, rtol=1e-5)
npt.assert_allclose((ipt.risk_difference['95%LCL'][1], ipt.risk_difference['95%UCL'][1]), sas_rd_ci, rtol=1e-4)
def test_match_sas_unstabilized(self, sdata):
sas_w_sum = 1038.051
sas_rd = -0.081519085
sas_rd_ci = -0.156199938, -0.006838231
model = 'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0'
ipt = IPTW(sdata, treatment='art', stabilized=False)
ipt.regression_models(model)
ipt.fit()
sdata['iptw'] = ipt.Weight
npt.assert_allclose(np.sum(sdata.dropna()['iptw']),
sas_w_sum,
rtol=1e-4)
# Estimating GEE
ind = sm.cov_struct.Independence()
f = sm.families.family.Binomial(sm.families.links.identity)
linrisk = smf.gee('dead ~ art',
sdata['id'],
sdata,
cov_struct=ind,
family=f,
weights=sdata['iptw']).fit()
npt.assert_allclose(linrisk.params[1], sas_rd, rtol=1e-5)
npt.assert_allclose(
(linrisk.conf_int()[0][1], linrisk.conf_int()[1][1]),
sas_rd_ci,
rtol=1e-4)
def test_unstabilized_weights_w_weights(self, data):
data['weights'] = 2
ipt = IPTW(data, treatment='A', weights='weights', stabilized=False)
ipt.regression_models(model_denominator='L', print_results=False)
ipt.fit()
npt.assert_allclose(ipt.Weight,
[6, 6, 8 / 3, 8 / 3, 8 / 3, 3, 3, 3, 3, 8])
def test_match_sas_smr_u_stabilized(self, sdata):
sas_rd = -0.080048197
sas_rd_ci = -0.153567335, -0.006529058
model = 'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0'
ipt = IPTW(sdata,
treatment='art',
standardize='unexposed',
stabilized=True)
ipt.regression_models(model)
ipt.fit()
sdata['iptw'] = ipt.Weight
# Estimating GEE
ind = sm.cov_struct.Independence()
f = sm.families.family.Binomial(sm.families.links.identity)
linrisk = smf.gee('dead ~ art',
sdata['id'],
sdata,
cov_struct=ind,
family=f,
weights=sdata['iptw']).fit()
npt.assert_allclose(linrisk.params[1], sas_rd, rtol=1e-5)
npt.assert_allclose(
(linrisk.conf_int()[0][1], linrisk.conf_int()[1][1]),
sas_rd_ci,
rtol=1e-4)
def test_stabilized_weights(self, data):
ipt = IPTW(data, treatment='A', stabilized=True)
ipt.regression_models(model_denominator='L', print_results=False)
ipt.fit()
npt.assert_allclose(
ipt.Weight,
[1.5, 1.5, 2 / 3, 2 / 3, 2 / 3, 3 / 4, 3 / 4, 3 / 4, 3 / 4, 2])
def test_probability_calc(self, data):
ipt = IPTW(data, treatment='A', stabilized=True)
ipt.regression_models(model_denominator='L', print_results=False)
ipt.fit()
pd = ipt.ProbabilityDenominator
pn = ipt.ProbabilityNumerator
npt.assert_allclose(pn, [0.5]*10)
npt.assert_allclose(pd, [1/3, 1/3, 0.75, 0.75, 0.75, 1/3, 1/3, 1/3, 1/3, 0.75])
def test_standardized_differences(self, sdata):
ipt = IPTW(sdata, treatment='art', stabilized=True)
ipt.regression_models('male + age0 + cd40 + dvl0')
ipt.fit()
smd = ipt.standardized_mean_differences()
npt.assert_allclose(np.array(smd['smd_u']),
np.array([-0.015684, 0.022311, -0.4867, -0.015729]),
rtol=1e-4) # for unweighted
# TODO find R package to test these weighted SMD's
npt.assert_allclose(np.array(smd['smd_w']),
np.array([-0.097789, -0.012395, -0.018591, 0.050719]),
rtol=1e-4) # for weighted
def test_match_iptw(self, sdata):
model = 'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0'
sdata = sdata.dropna().copy()
# Estimating Marginal Structural Model
ipt = IPTW(sdata, treatment='art', outcome='dead')
ipt.treatment_model(model_denominator=model, stabilized=False, print_results=False)
ipt.marginal_structural_model('art')
ipt.fit()
# Estimating 'Stochastic Treatment'
sipw = StochasticIPTW(sdata, treatment='art', outcome='dead')
sipw.treatment_model(model=model, print_results=False)
sipw.fit(p=1.0)
r_all = sipw.marginal_outcome
sipw.fit(p=0.0)
r_non = sipw.marginal_outcome
npt.assert_allclose(ipt.risk_difference['RD'][1], r_all - r_non, atol=1e-7)
def test_match_iptw_continuous(self, cdata):
model = 'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0'
cdata = cdata.dropna().copy()
# Estimating Marginal Structural Model
ipt = IPTW(cdata, treatment='art', outcome='cd4_wk45')
ipt.treatment_model(model_denominator=model, stabilized=False, print_results=False)
ipt.marginal_structural_model('art')
ipt.fit()
# Estimating 'Stochastic Treatment'
sipw = StochasticIPTW(cdata, treatment='art', outcome='cd4_wk45')
sipw.treatment_model(model=model, print_results=False)
sipw.fit(p=1.0)
r_all = sipw.marginal_outcome
sipw.fit(p=0.0)
r_non = sipw.marginal_outcome
npt.assert_allclose(ipt.average_treatment_effect['ATE'][1], r_all - r_non, atol=1e-4)
def test_custom_models(self, sdata):
model = 'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0'
logd = LogisticRegression(penalty='l1', C=1.0, random_state=203)
ipt = IPTW(sdata, treatment='art', standardize='unexposed', stabilized=True)
ipt.regression_models(model, custom_model_denominator=logd)
ipt.fit()
sdata['iptw'] = ipt.Weight
# Estimating GEE
ind = sm.cov_struct.Independence()
f = sm.families.family.Binomial(sm.families.links.identity)
smf.gee('dead ~ art', sdata['id'], sdata, cov_struct=ind, family=f, weights=sdata['iptw']).fit()
def test_match_iptw_continuous(self, cdata):
model = 'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0'
cdata = cdata.dropna().copy()
# Estimating Marginal Structural Model
ipt = IPTW(cdata, treatment='art', stabilized=False)
ipt.regression_models(model)
ipt.fit()
cdata['iptw'] = ipt.Weight
ind = sm.cov_struct.Independence()
f = sm.families.family.Gaussian()
linrisk = smf.gee('cd4_wk45 ~ art', cdata['id'], cdata, cov_struct=ind, family=f, weights=cdata['iptw']).fit()
# Estimating 'Stochastic Treatment'
sipw = StochasticIPTW(cdata, treatment='art', outcome='cd4_wk45')
sipw.treatment_model(model='male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0',
print_results=False)
sipw.fit(p=1.0)
r_all = sipw.marginal_outcome
sipw.fit(p=0.0)
r_non = sipw.marginal_outcome
npt.assert_allclose(linrisk.params[1], r_all - r_non, atol=1e-4)
def causal_check():
# 9) Check IPTW plots
data = load_sample_data(False)
data[['cd4_rs1', 'cd4_rs2']] = spline(data, 'cd40', n_knots=3, term=2, restricted=True)
data[['age_rs1', 'age_rs2']] = spline(data, 'age0', n_knots=3, term=2, restricted=True)
ipt = IPTW(data, treatment='art', stabilized=True)
ipt.regression_models('male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0')
ipt.fit()
ipt.plot_love()
plt.tight_layout()
plt.show()
ipt.plot_kde()
plt.show()
ipt.plot_kde(measure='logit')
plt.show()
ipt.plot_boxplot()
plt.show()
ipt.plot_boxplot(measure='logit')
plt.show()
def causal_check():
data = load_sample_data(False).drop(columns=['cd4_wk45'])
data[['cd4_rs1', 'cd4_rs2']] = spline(data,
'cd40',
n_knots=3,
term=2,
restricted=True)
data[['age_rs1', 'age_rs2']] = spline(data,
'age0',
n_knots=3,
term=2,
restricted=True)
# Check TimeFixedGFormula diagnostics
g = TimeFixedGFormula(data, exposure='art', outcome='dead')
g.outcome_model(
model=
'art + male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0'
)
g.run_diagnostics(decimal=3)
# Check IPTW plots
ipt = IPTW(data, treatment='art', outcome='dead')
ipt.treatment_model(
'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0',
stabilized=True)
ipt.marginal_structural_model('art')
ipt.fit()
ipt.plot_love()
plt.tight_layout()
plt.show()
ipt.plot_kde()
plt.show()
ipt.plot_kde(measure='logit')
plt.show()
ipt.plot_boxplot()
plt.show()
ipt.plot_boxplot(measure='logit')
plt.show()
ipt.run_diagnostics()
# Check AIPTW Diagnostics
aipw = AIPTW(data, exposure='art', outcome='dead')
aipw.exposure_model(
'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0')
aipw.outcome_model(
'art + male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0'
)
aipw.fit()
aipw.run_diagnostics()
aipw.plot_kde(to_plot='exposure')
plt.show()
aipw.plot_kde(to_plot='outcome')
plt.show()
aipw.plot_love()
plt.show()
# Check TMLE diagnostics
tmle = TMLE(data, exposure='art', outcome='dead')
tmle.exposure_model(
'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0')
tmle.outcome_model(
'art + male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0'
)
tmle.fit()
tmle.run_diagnostics()
tmle.plot_kde(to_plot='exposure')
plt.show()
tmle.plot_kde(to_plot='outcome')
plt.show()
tmle.plot_love()
plt.show()
# Check SurvivalGFormula plots
df = load_sample_data(False).drop(columns=['cd4_wk45'])
df['t'] = np.round(df['t']).astype(int)
df = pd.DataFrame(np.repeat(df.values, df['t'], axis=0),
columns=df.columns)
df['t'] = df.groupby('id')['t'].cumcount() + 1
df.loc[((df['dead'] == 1) & (df['id'] != df['id'].shift(-1))), 'd'] = 1
df['d'] = df['d'].fillna(0)
df['t_sq'] = df['t']**2
df['t_cu'] = df['t']**3
sgf = SurvivalGFormula(df,
idvar='id',
exposure='art',
outcome='d',
time='t')
sgf.outcome_model(
model='art + male + age0 + cd40 + dvl0 + t + t_sq + t_cu')
sgf.fit(treatment='all')
sgf.plot()
plt.show()
sgf.plot(c='r', linewidth=3, alpha=0.8)
plt.show()
def causal_check():
# Check IPTW plots
data = load_sample_data(False)
data[['cd4_rs1', 'cd4_rs2']] = spline(data,
'cd40',
n_knots=3,
term=2,
restricted=True)
data[['age_rs1', 'age_rs2']] = spline(data,
'age0',
n_knots=3,
term=2,
restricted=True)
ipt = IPTW(data, treatment='art', stabilized=True)
ipt.regression_models(
'male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0')
ipt.fit()
ipt.plot_love()
plt.tight_layout()
plt.show()
ipt.plot_kde()
plt.show()
ipt.plot_kde(measure='logit')
plt.show()
ipt.plot_boxplot()
plt.show()
ipt.plot_boxplot(measure='logit')
plt.show()
# Check SurvivalGFormula plots
df = load_sample_data(False).drop(columns=['cd4_wk45'])
df['t'] = np.round(df['t']).astype(int)
df = pd.DataFrame(np.repeat(df.values, df['t'], axis=0),
columns=df.columns)
df['t'] = df.groupby('id')['t'].cumcount() + 1
df.loc[((df['dead'] == 1) & (df['id'] != df['id'].shift(-1))), 'd'] = 1
df['d'] = df['d'].fillna(0)
df['t_sq'] = df['t']**2
df['t_cu'] = df['t']**3
sgf = SurvivalGFormula(df,
idvar='id',
exposure='art',
outcome='d',
time='t')
sgf.outcome_model(
model='art + male + age0 + cd40 + dvl0 + t + t_sq + t_cu')
sgf.fit(treatment='all')
sgf.plot()
plt.show()
sgf.plot(c='r', linewidth=3, alpha=0.8)
plt.show()
def test_unstabilized_positivity_warning(self, data):
ipt = IPTW(data, treatment='A', stabilized=False)
ipt.regression_models(model_denominator='L', print_results=False)
ipt.fit()
with pytest.warns(UserWarning):
ipt.positivity()
print_results=False)
g.fit(treatment='all')
r_all = g.marginal_outcome
g.fit(treatment='none')
r_none = g.marginal_outcome
rd_results.append(r_all - r_none)
se = np.std(rd_results)
print('95% LCL', riskd - 1.96*se)
print('95% UCL', riskd + 1.96*se)
#############################
# IPTW
from zepid.causal.ipw import IPTW
iptw = IPTW(df, treatment='art', outcome='dead')
iptw.treatment_model('male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0',
bound=0.01, print_results=False)
iptw.marginal_structural_model('art')
iptw.fit()
iptw.summary()
#############################
# AIPTW
from zepid.causal.doublyrobust import AIPTW
aipw = AIPTW(df, exposure='art', outcome='dead')
# Treatment model
aipw.exposure_model('male + age0 + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0',
print_results=False, bound=0.01)
# Outcome model
