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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
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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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46 changes: 23 additions & 23 deletions econml/tests/test_discrete_outcome.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,35 +27,35 @@ 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
count_within_interval = 0

ests = [
LinearDML(discrete_outcome=discrete_outcome, discrete_treatment=discrete_treatment),
CausalForestDML(discrete_outcome=discrete_outcome, discrete_treatment=discrete_treatment),
LinearDRLearner(discrete_outcome=discrete_outcome)
]
for _ in range(num_iterations):

for est in ests:
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 = est.ate(X=W)
ate_lb, ate_ub = est.ate_interval(X=W)
ests = [
LinearDML(discrete_outcome=discrete_outcome, discrete_treatment=discrete_treatment),
CausalForestDML(discrete_outcome=discrete_outcome, discrete_treatment=discrete_treatment),
LinearDRLearner(discrete_outcome=discrete_outcome)
]

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

if isinstance(est, LinearDRLearner):
est.summary(T=1)
else:
est.summary()
if isinstance(est, CausalForestDML):
est.fit(Y, D, X=W)
ate_lb, ate_ub = est.ate_interval(X=W)

proportion_in_interval = ((ate_lb < true_ate) & (true_ate < ate_ub)).mean()
np.testing.assert_array_less(0.50, proportion_in_interval)
else:
est.fit(Y, D, W=W)
ate_lb, ate_ub = est.ate_interval()

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

assert count_within_interval >= 8, f"True ATE falls within the interval bounds only {count_within_interval} times out of {num_iterations}"

# accuracy test, DML
def test_accuracy_iv(self):
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