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Fix the test_accuracy function by modifying the assertion logic #867

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merged 3 commits into from
Apr 10, 2024
Merged

Fix the test_accuracy function by modifying the assertion logic #867

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cd82563
Fix the test_accuracy function by modifying the assertion logic
kgao Mar 29, 2024
ae66642
Update: 1.Apply the logic for each estimator 2. Apply the logic for t…
kgao Apr 9, 2024
78d3f18
update: cleanup - linting and code format
kgao Apr 10, 2024
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74 changes: 46 additions & 28 deletions econml/tests/test_discrete_outcome.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,9 +27,7 @@ def test_accuracy(self):
discrete_outcome = True
discrete_treatment = True
true_ate = 0.3
W = np.random.uniform(-1, 1, size=(n, 1))
D = np.random.binomial(1, .5 + .1 * W[:, 0], size=(n,))
Y = np.random.binomial(1, .5 + true_ate * D + .1 * W[:, 0], size=(n,))
num_iterations = 10

ests = [
LinearDML(discrete_outcome=discrete_outcome, discrete_treatment=discrete_treatment),
Expand All @@ -39,34 +37,42 @@ def test_accuracy(self):

for est in ests:

if isinstance(est, CausalForestDML):
est.fit(Y, D, X=W)
ate = est.ate(X=W)
ate_lb, ate_ub = est.ate_interval(X=W)
count_within_interval = 0

else:
est.fit(Y, D, W=W)
ate = est.ate()
ate_lb, ate_ub = est.ate_interval()
for _ in range(num_iterations):

if isinstance(est, LinearDRLearner):
est.summary(T=1)
else:
est.summary()
W = np.random.uniform(-1, 1, size=(n, 1))
D = np.random.binomial(1, .5 + .1 * W[:, 0], size=(n,))
Y = np.random.binomial(1, .5 + true_ate * D + .1 * W[:, 0], size=(n,))

if isinstance(est, CausalForestDML):
est.fit(Y, D, X=W)
ate_lb, ate_ub = est.ate_interval(X=W)

else:
est.fit(Y, D, W=W)
ate_lb, ate_ub = est.ate_interval()

if isinstance(est, LinearDRLearner):
est.summary(T=1)
else:
est.summary()

if ate_lb <= true_ate <= ate_ub:
count_within_interval += 1

proportion_in_interval = ((ate_lb < true_ate) & (true_ate < ate_ub)).mean()
np.testing.assert_array_less(0.50, proportion_in_interval)
assert count_within_interval >= 7, (
f"{est.__class__.__name__}: True ATE falls within the interval bounds "
f"only {count_within_interval} times out of {num_iterations}"
)

# accuracy test, DML
def test_accuracy_iv(self):
n = 10000
n = 1000
discrete_outcome = True
discrete_treatment = True
true_ate = 0.3
W = np.random.uniform(-1, 1, size=(n, 1))
Z = np.random.uniform(-1, 1, size=(n, 1))
D = np.random.binomial(1, .5 + .1 * W[:, 0] + .1 * Z[:, 0], size=(n,))
Y = np.random.binomial(1, .5 + true_ate * D + .1 * W[:, 0], size=(n,))
num_iterations = 10

ests = [
OrthoIV(discrete_outcome=discrete_outcome, discrete_treatment=discrete_treatment),
Expand All @@ -75,14 +81,26 @@ def test_accuracy_iv(self):

for est in ests:

est.fit(Y, D, W=W, Z=Z)
ate = est.ate()
ate_lb, ate_ub = est.ate_interval()
count_within_interval = 0

for _ in range(num_iterations):

W = np.random.uniform(-1, 1, size=(n, 1))
Z = np.random.uniform(-1, 1, size=(n, 1))
D = np.random.binomial(1, .5 + .1 * W[:, 0] + .1 * Z[:, 0], size=(n,))
Y = np.random.binomial(1, .5 + true_ate * D + .1 * W[:, 0], size=(n,))

est.fit(Y, D, W=W, Z=Z)
ate_lb, ate_ub = est.ate_interval()
est.summary()

est.summary()
if ate_lb <= true_ate <= ate_ub:
count_within_interval += 1

proportion_in_interval = ((ate_lb < true_ate) & (true_ate < ate_ub)).mean()
np.testing.assert_array_less(0.50, proportion_in_interval)
assert count_within_interval >= 7, (
f"{est.__class__.__name__}: True ATE falls within the interval bounds "
f"only {count_within_interval} times out of {num_iterations}"
)

def test_string_outcome(self):
n = 100
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