def test_match_r_ltmle3(self):
df = load_longitudinal_data()
icgf = IterativeCondGFormula(df, exposures=['A1', 'A2', 'A3'], outcomes=['Y1', 'Y2', 'Y3'])
icgf.outcome_model(models=['A1 + L1', 'A2 + A1 + L2', 'A3 + A2 + L3'], print_results=False)
icgf.fit(treatments=[1, 1, 1])
npt.assert_allclose(icgf.marginal_outcome, 0.4334696, rtol=1e-5)
icgf.fit(treatments=[0, 0, 0])
npt.assert_allclose(icgf.marginal_outcome, 0.6282985, rtol=1e-5)
def test_match_r_custom_treatment(self):
df = load_longitudinal_data()
icgf = IterativeCondGFormula(df, exposures=['A1', 'A2', 'A3'], outcomes=['Y1', 'Y2', 'Y3'])
icgf.outcome_model(models=['A1 + L1', 'A2 + L2', 'A3 + L3'], print_results=False)
icgf.fit(treatments=[1, 0, 1])
npt.assert_allclose(icgf.marginal_outcome, 0.4916937, rtol=1e-5)
icgf.fit(treatments=[0, 1, 0])
npt.assert_allclose(icgf.marginal_outcome, 0.5634683, rtol=1e-5)
def test_treatment_dimension_error2(self):
df = load_longitudinal_data()
icgf = IterativeCondGFormula(df,
exposures=['A1', 'A2', 'A3'],
outcomes=['Y1', 'Y2', 'Y3'])
icgf.outcome_model(models=['A1 + L1', 'A2 + L2', 'A3 + L3'],
print_results=False)
with pytest.raises(ValueError):
icgf.fit(treatments=[[1, 1, 1], [0, 0, 0]])
def test_iterative_for_single_t(self, sim_t_fixed_data):
# Estimating sequential regression for single t
gt = IterativeCondGFormula(sim_t_fixed_data, exposures=['A'], outcomes=['Y'])
gt.outcome_model(['A + W1_sq + W2 + W3'], print_results=False)
gt.fit(treatments=[1])
# Estimating with TimeFixedGFormula
gf = TimeFixedGFormula(sim_t_fixed_data, exposure='A', outcome='Y')
gf.outcome_model(model='A + W1_sq + W2 + W3', print_results=False)
gf.fit(treatment='all')
# Expected behavior; same results between the estimation methods
npt.assert_allclose(gf.marginal_outcome, gt.marginal_outcome)
def test_error_iterative_no_outcome(self, simple_data):
g = IterativeCondGFormula(simple_data, exposures=['A1'], outcomes=['Y1'])
with pytest.raises(ValueError):
g.fit(treatments=[1])