def test_transport(self, df_r):
aipw = AIPSW(df_r, exposure='A', outcome='Y', selection='S', generalize=False)
aipw.weight_model('L + W_sq', print_results=False)
aipw.outcome_model('A + L + L:A + W_sq + W_sq:A + W_sq:A:L', print_results=False)
aipw.fit()
npt.assert_allclose(aipw.risk_difference, 0.05479, atol=1e-5)
npt.assert_allclose(aipw.risk_ratio, 1.16352, atol=1e-4)
def test_transport_conf(self, df_iptw):
aipw = AIPSW(df_iptw, exposure='A', outcome='Y', selection='S', generalize=False, weights='iptw')
aipw.weight_model('L + W_sq', print_results=False)
aipw.outcome_model('A + L + L:A + W_sq + W_sq:A + W_sq:A:L', print_results=False)
aipw.fit()
npt.assert_allclose(aipw.risk_difference, 0.041407, atol=1e-5)
npt.assert_allclose(aipw.risk_ratio, 1.120556, atol=1e-4)
def test_generalize(self, df_r):
aipw = AIPSW(df_r,
exposure='A',
outcome='Y',
selection='S',
generalize=True)
aipw.sampling_model('L + W_sq', print_results=False)
aipw.outcome_model('A + L + L:A + W_sq + W_sq:A + W_sq:A:L',
print_results=False)
aipw.fit()
npt.assert_allclose(aipw.risk_difference, 0.061382, atol=1e-5)
npt.assert_allclose(aipw.risk_ratio, 1.193161, atol=1e-4)
def test_generalize_conf(self, df_c):
aipw = AIPSW(df_c,
exposure='A',
outcome='Y',
selection='S',
generalize=True)
aipw.sampling_model('L + W_sq', print_results=False)
aipw.treatment_model('L', print_results=False)
aipw.outcome_model('A + L + L:A + W_sq + W_sq:A + W_sq:A:L',
print_results=False)
aipw.fit()
npt.assert_allclose(aipw.risk_difference, 0.048129, atol=1e-5)
npt.assert_allclose(aipw.risk_ratio, 1.146787, atol=1e-4)
def test_no_model_error(self, df_c):
aipw = AIPSW(df_c, exposure='A', outcome='Y', selection='S', generalize=True)
with pytest.raises(ValueError):
aipw.fit()
aipw.weight_model('L', print_results=False)
with pytest.raises(ValueError):
aipw.fit()
aipw = AIPSW(df_c, exposure='A', outcome='Y', selection='S', generalize=True)
aipw.outcome_model('A + L')
with pytest.raises(ValueError):
aipw.fit()