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Merged
merged 18 commits into from
Oct 11, 2024
Merged

Support for treatment strategies with interventions at non-consecutiv… #292

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89eeaed
Support for treatment strategies with interventions at non-consecutiv…
jmafoster1 Sep 18, 2024
5615169
pylint
jmafoster1 Sep 18, 2024
50b2bf2
Fixed strategy equality bug
jmafoster1 Sep 18, 2024
7613e6b
tests pass again
jmafoster1 Sep 18, 2024
190b239
Maybe fixed the estimation bug (one test will fail)
jmafoster1 Sep 20, 2024
72724eb
Seems to be working
jmafoster1 Sep 30, 2024
9b5f075
Vectorised preprocessing
jmafoster1 Oct 2, 2024
f505dd7
Optimisation and bug fixes
jmafoster1 Oct 7, 2024
beb9547
Removed exception handling in causal test case to catch it further up…
jmafoster1 Oct 7, 2024
d12cd0b
Fixed the tests
jmafoster1 Oct 7, 2024
559802f
pylint
jmafoster1 Oct 7, 2024
4704d19
pylint
jmafoster1 Oct 7, 2024
42361f2
pylint
jmafoster1 Oct 7, 2024
943605f
pylint
jmafoster1 Oct 7, 2024
b2e33fc
removed hardcoded log directory
jmafoster1 Oct 7, 2024
ddc3c60
And another one
jmafoster1 Oct 7, 2024
1d4afee
codecov
jmafoster1 Oct 7, 2024
e6d46cd
tests pass again
jmafoster1 Oct 11, 2024
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283 changes: 197 additions & 86 deletions causal_testing/estimation/ipcw_estimator.py
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77 changes: 0 additions & 77 deletions causal_testing/specification/capabilities.py
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@f-allian f-allian Sep 19, 2024 •
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@f-allian, is the codecov just failing on this because the codebase has got slightly bigger? It says I have 100% coverage of the patch, so that's all I can think of, but adding in extra code with 100% coverage should be fine right?

@jmafoster1 Looks like the coverage is failing because you've deleted causal_testing/specification/capabilities.py. Do you know if this was intentional? If so, recommitting it should fix it.

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Yes, this was intentional. It is no longer required as the new functionality requires timesteps to be specified explicitly, which is what I wrote capabilities.py to handle implicitly.

This file was deleted.

23 changes: 7 additions & 16 deletions causal_testing/testing/causal_test_case.py
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Expand Up @@ -2,7 +2,6 @@

import logging
from typing import Any
import numpy as np

from causal_testing.specification.variable import Variable
from causal_testing.testing.causal_test_outcome import CausalTestOutcome
Expand Down Expand Up @@ -81,21 +80,13 @@ def _return_causal_test_results(self, estimator) -> CausalTestResult:
if not hasattr(estimator, f"estimate_{self.estimate_type}"):
raise AttributeError(f"{estimator.__class__} has no {self.estimate_type} method.")
estimate_effect = getattr(estimator, f"estimate_{self.estimate_type}")
try:
effect, confidence_intervals = estimate_effect(**self.estimate_params)
return CausalTestResult(
estimator=estimator,
test_value=TestValue(self.estimate_type, effect),
effect_modifier_configuration=self.effect_modifier_configuration,
confidence_intervals=confidence_intervals,
)
except np.linalg.LinAlgError:
return CausalTestResult(
estimator=estimator,
test_value=TestValue(self.estimate_type, None),
effect_modifier_configuration=self.effect_modifier_configuration,
confidence_intervals=None,
)
effect, confidence_intervals = estimate_effect(**self.estimate_params)
return CausalTestResult(
estimator=estimator,
test_value=TestValue(self.estimate_type, effect),
effect_modifier_configuration=self.effect_modifier_configuration,
confidence_intervals=confidence_intervals,
)

def __str__(self):
treatment_config = {self.treatment_variable.name: self.treatment_value}
Expand Down
1 change: 0 additions & 1 deletion tests/estimation_tests/test_cubic_spline_estimator.py
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Expand Up @@ -4,7 +4,6 @@
import matplotlib.pyplot as plt
from causal_testing.specification.variable import Input
from causal_testing.utils.validation import CausalValidator
from causal_testing.specification.capabilities import TreatmentSequence

from causal_testing.estimation.cubic_spline_estimator import CubicSplineRegressionEstimator
from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
Expand Down
1 change: 0 additions & 1 deletion tests/estimation_tests/test_instrumental_variable_estimator.py
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Expand Up @@ -4,7 +4,6 @@
import matplotlib.pyplot as plt
from causal_testing.specification.variable import Input
from causal_testing.utils.validation import CausalValidator
from causal_testing.specification.capabilities import TreatmentSequence

from causal_testing.estimation.instrumental_variable_estimator import InstrumentalVariableEstimator

Expand Down
59 changes: 51 additions & 8 deletions tests/estimation_tests/test_ipcw_estimator.py
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Expand Up @@ -4,7 +4,6 @@
import matplotlib.pyplot as plt
from causal_testing.specification.variable import Input
from causal_testing.utils.validation import CausalValidator
from causal_testing.specification.capabilities import TreatmentSequence

from causal_testing.estimation.ipcw_estimator import IPCWEstimator

Expand All @@ -16,8 +15,8 @@ class TestIPCWEstimator(unittest.TestCase):

def test_estimate_hazard_ratio(self):
timesteps_per_intervention = 1
control_strategy = TreatmentSequence(timesteps_per_intervention, [("t", 0), ("t", 0), ("t", 0)])
treatment_strategy = TreatmentSequence(timesteps_per_intervention, [("t", 1), ("t", 1), ("t", 1)])
control_strategy = [[t, "t", 0] for t in range(1, 4, timesteps_per_intervention)]
treatment_strategy = [[t, "t", 1] for t in range(1, 4, timesteps_per_intervention)]
outcome = "outcome"
fit_bl_switch_formula = "xo_t_do ~ time"
df = pd.read_csv("tests/resources/data/temporal_data.csv")
Expand All @@ -34,12 +33,12 @@ def test_estimate_hazard_ratio(self):
eligibility=None,
)
estimate, intervals = estimation_model.estimate_hazard_ratio()
self.assertEqual(estimate["trtrand"], 1.0)
self.assertEqual(round(estimate["trtrand"], 3), 1.936)

def test_invalid_treatment_strategies(self):
timesteps_per_intervention = 1
control_strategy = TreatmentSequence(timesteps_per_intervention, [("t", 0), ("t", 0), ("t", 0)])
treatment_strategy = TreatmentSequence(timesteps_per_intervention, [("t", 1), ("t", 1), ("t", 1)])
control_strategy = [[t, "t", 0] for t in range(1, 4, timesteps_per_intervention)]
treatment_strategy = [[t, "t", 1] for t in range(1, 4, timesteps_per_intervention)]
outcome = "outcome"
fit_bl_switch_formula = "xo_t_do ~ time"
df = pd.read_csv("tests/resources/data/temporal_data.csv")
Expand All @@ -60,8 +59,8 @@ def test_invalid_treatment_strategies(self):

def test_invalid_fault_t_do(self):
timesteps_per_intervention = 1
control_strategy = TreatmentSequence(timesteps_per_intervention, [("t", 0), ("t", 0), ("t", 0)])
treatment_strategy = TreatmentSequence(timesteps_per_intervention, [("t", 1), ("t", 1), ("t", 1)])
control_strategy = [[t, "t", 0] for t in range(1, 4, timesteps_per_intervention)]
treatment_strategy = [[t, "t", 1] for t in range(1, 4, timesteps_per_intervention)]
outcome = "outcome"
fit_bl_switch_formula = "xo_t_do ~ time"
df = pd.read_csv("tests/resources/data/temporal_data.csv")
Expand All @@ -80,3 +79,47 @@ def test_invalid_fault_t_do(self):
estimation_model.df["fault_t_do"] = 0
with self.assertRaises(ValueError):
estimate, intervals = estimation_model.estimate_hazard_ratio()

def test_no_individual_began_control_strategy(self):
timesteps_per_intervention = 1
control_strategy = [[t, "t", 0] for t in range(1, 4, timesteps_per_intervention)]
treatment_strategy = [[t, "t", 1] for t in range(1, 4, timesteps_per_intervention)]
outcome = "outcome"
fit_bl_switch_formula = "xo_t_do ~ time"
df = pd.read_csv("tests/resources/data/temporal_data.csv")
df["t"] = 1
df["ok"] = df["outcome"] == 1
with self.assertRaises(ValueError):
estimation_model = IPCWEstimator(
df,
timesteps_per_intervention,
control_strategy,
treatment_strategy,
outcome,
"ok",
fit_bl_switch_formula=fit_bl_switch_formula,
fit_bltd_switch_formula=fit_bl_switch_formula,
eligibility=None,
)

def test_no_individual_began_treatment_strategy(self):
timesteps_per_intervention = 1
control_strategy = [[t, "t", 0] for t in range(1, 4, timesteps_per_intervention)]
treatment_strategy = [[t, "t", 1] for t in range(1, 4, timesteps_per_intervention)]
outcome = "outcome"
fit_bl_switch_formula = "xo_t_do ~ time"
df = pd.read_csv("tests/resources/data/temporal_data.csv")
df["t"] = 0
df["ok"] = df["outcome"] == 1
with self.assertRaises(ValueError):
estimation_model = IPCWEstimator(
df,
timesteps_per_intervention,
control_strategy,
treatment_strategy,
outcome,
"ok",
fit_bl_switch_formula=fit_bl_switch_formula,
fit_bltd_switch_formula=fit_bl_switch_formula,
eligibility=None,
)
1 change: 0 additions & 1 deletion tests/estimation_tests/test_linear_regression_estimator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,6 @@
import matplotlib.pyplot as plt
from causal_testing.specification.variable import Input
from causal_testing.utils.validation import CausalValidator
from causal_testing.specification.capabilities import TreatmentSequence

from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
from causal_testing.estimation.genetic_programming_regression_fitter import reciprocal
Expand Down
1 change: 0 additions & 1 deletion tests/estimation_tests/test_logistic_regression_estimator.py
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Expand Up @@ -4,7 +4,6 @@
import matplotlib.pyplot as plt
from causal_testing.specification.variable import Input
from causal_testing.utils.validation import CausalValidator
from causal_testing.specification.capabilities import TreatmentSequence
from causal_testing.estimation.logistic_regression_estimator import LogisticRegressionEstimator


Expand Down
36 changes: 0 additions & 36 deletions tests/specification_tests/test_capabilities.py
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This file was deleted.

14 changes: 7 additions & 7 deletions tests/testing_tests/test_causal_test_adequacy.py
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Expand Up @@ -16,7 +16,6 @@
from causal_testing.specification.variable import Input, Output, Meta
from causal_testing.specification.scenario import Scenario
from causal_testing.specification.causal_specification import CausalSpecification
from causal_testing.specification.capabilities import TreatmentSequence
from causal_testing.testing.causal_test_adequacy import DataAdequacy


Expand Down Expand Up @@ -112,8 +111,8 @@ def test_data_adequacy_cateogorical(self):

def test_data_adequacy_group_by(self):
timesteps_per_intervention = 1
control_strategy = TreatmentSequence(timesteps_per_intervention, [("t", 0), ("t", 0), ("t", 0)])
treatment_strategy = TreatmentSequence(timesteps_per_intervention, [("t", 1), ("t", 1), ("t", 1)])
control_strategy = [[t, "t", 0] for t in range(1, 4, timesteps_per_intervention)]
treatment_strategy = [[t, "t", 1] for t in range(1, 4, timesteps_per_intervention)]
outcome = "outcome"
fit_bl_switch_formula = "xo_t_do ~ time"
df = pd.read_csv("tests/resources/data/temporal_data.csv")
Expand Down Expand Up @@ -145,11 +144,12 @@ def test_data_adequacy_group_by(self):
causal_test_result = causal_test_case.execute_test(estimation_model, None)
adequacy_metric = DataAdequacy(causal_test_case, estimation_model, group_by="id")
adequacy_metric.measure_adequacy()
causal_test_result.adequacy = adequacy_metric
print(causal_test_result.adequacy.to_dict())
adequacy_dict = adequacy_metric.to_dict()
self.assertEqual(round(adequacy_dict["kurtosis"]["trtrand"], 3), -0.336)
adequacy_dict.pop("kurtosis")
self.assertEqual(
causal_test_result.adequacy.to_dict(),
{"kurtosis": {"trtrand": 0.0}, "bootstrap_size": 100, "passing": 0, "successful": 95},
adequacy_dict,
{"bootstrap_size": 100, "passing": 28, "successful": 95},
)

def test_dag_adequacy_dependent(self):
Expand Down
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