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Merged
TomeHirata merged 1 commit into from
Aug 20, 2026
Merged

docs: convert RST code-block directives in docstrings to Markdown #132

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158 changes: 77 additions & 81 deletions dte_adj/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,36 +60,34 @@ def predict_dte(
- Upper bounds (np.ndarray): Upper confidence interval bounds

Example:
.. code-block:: python

import numpy as np
from dte_adj import SimpleDistributionEstimator

# Generate sample data
X = np.random.randn(1000, 5)
D = np.random.binomial(1, 0.5, 1000)
Y = X[:, 0] + 2 * D + np.random.randn(1000)

# Fit estimator
estimator = SimpleDistributionEstimator()
estimator.fit(X, D, Y)

# Compute DTE
locations = np.linspace(Y.min(), Y.max(), 20)
dte, lower, upper = estimator.predict_dte(
target_treatment_arm=1,
control_treatment_arm=0,
locations=locations,
variance_type="moment"
)
```python
import numpy as np
from dte_adj import SimpleDistributionEstimator

# Generate sample data
X = np.random.randn(1000, 5)
D = np.random.binomial(1, 0.5, 1000)
Y = X[:, 0] + 2 * D + np.random.randn(1000)

# Fit estimator
estimator = SimpleDistributionEstimator()
estimator.fit(X, D, Y)

# Compute DTE
locations = np.linspace(Y.min(), Y.max(), 20)
dte, lower, upper = estimator.predict_dte(
target_treatment_arm=1,
control_treatment_arm=0,
locations=locations,
variance_type="moment"
)

print(f"DTE shape: {dte.shape}") # Should match locations.shape
print(f"Average DTE: {dte.mean():.3f}")
print(f"DTE shape: {dte.shape}") # Should match locations.shape
print(f"Average DTE: {dte.mean():.3f}")
```
"""
if locations is None:
locations = _infer_default_locations(
self.outcomes, for_intervals=False
)
locations = _infer_default_locations(self.outcomes, for_intervals=False)
self.last_locations = locations
return self._compute_dtes(
target_treatment_arm,
Expand Down Expand Up @@ -143,38 +141,36 @@ def predict_pte(
- Upper bounds (np.ndarray): Upper confidence interval bounds

Example:
.. code-block:: python

import numpy as np
from dte_adj import SimpleDistributionEstimator

# Generate sample data
X = np.random.randn(1000, 5)
D = np.random.binomial(1, 0.5, 1000)
Y = X[:, 0] + 2 * D + np.random.randn(1000)

# Fit estimator
estimator = SimpleDistributionEstimator()
estimator.fit(X, D, Y)

# Define interval boundaries
locations = np.array([-2, -1, 0, 1, 2]) # Creates intervals: (-2,-1], (-1,0], (0,1], (1,2]

# Compute PTE
pte, lower, upper = estimator.predict_pte(
target_treatment_arm=1,
control_treatment_arm=0,
locations=locations,
variance_type="moment"
)
```python
import numpy as np
from dte_adj import SimpleDistributionEstimator

# Generate sample data
X = np.random.randn(1000, 5)
D = np.random.binomial(1, 0.5, 1000)
Y = X[:, 0] + 2 * D + np.random.randn(1000)

# Fit estimator
estimator = SimpleDistributionEstimator()
estimator.fit(X, D, Y)

# Define interval boundaries
locations = np.array([-2, -1, 0, 1, 2]) # Creates intervals: (-2,-1], (-1,0], (0,1], (1,2]

# Compute PTE
pte, lower, upper = estimator.predict_pte(
target_treatment_arm=1,
control_treatment_arm=0,
locations=locations,
variance_type="moment"
)

print(f"PTE shape: {pte.shape}") # Should be (4,) for 4 intervals
print(f"Interval effects: {pte}")
print(f"PTE shape: {pte.shape}") # Should be (4,) for 4 intervals
print(f"Interval effects: {pte}")
```
"""
if locations is None:
locations = _infer_default_locations(
self.outcomes, for_intervals=True
)
locations = _infer_default_locations(self.outcomes, for_intervals=True)
self.last_locations = locations
return self._compute_ptes(
target_treatment_arm,
Expand Down Expand Up @@ -222,32 +218,32 @@ def predict_qte(
- Upper bounds (np.ndarray): Upper confidence interval bounds

Example:
.. code-block:: python

import numpy as np
from dte_adj import SimpleStratifiedDistributionEstimator

# Generate stratified sample data
X = np.random.randn(1000, 5)
strata = np.random.choice([0, 1, 2], size=1000)
D = np.random.binomial(1, 0.5, 1000)
Y = X[:, 0] + 2 * D + 0.5 * strata + np.random.randn(1000)

# Fit stratified estimator
estimator = SimpleStratifiedDistributionEstimator()
estimator.fit(X, D, Y, strata)

# Compute QTE at specific quantiles
quantiles = np.array([0.25, 0.5, 0.75]) # 25th, 50th, 75th percentiles
qte, lower, upper = estimator.predict_qte(
target_treatment_arm=1,
control_treatment_arm=0,
quantiles=quantiles,
n_bootstrap=100
)
```python
import numpy as np
from dte_adj import SimpleStratifiedDistributionEstimator

# Generate stratified sample data
X = np.random.randn(1000, 5)
strata = np.random.choice([0, 1, 2], size=1000)
D = np.random.binomial(1, 0.5, 1000)
Y = X[:, 0] + 2 * D + 0.5 * strata + np.random.randn(1000)

# Fit stratified estimator
estimator = SimpleStratifiedDistributionEstimator()
estimator.fit(X, D, Y, strata)

# Compute QTE at specific quantiles
quantiles = np.array([0.25, 0.5, 0.75]) # 25th, 50th, 75th percentiles
qte, lower, upper = estimator.predict_qte(
target_treatment_arm=1,
control_treatment_arm=0,
quantiles=quantiles,
n_bootstrap=100
)

print(f"QTE at quantiles {quantiles}: {qte}")
print(f"Median effect (50th percentile): {qte[1]:.3f}")
print(f"QTE at quantiles {quantiles}: {qte}")
print(f"Median effect (50th percentile): {qte[1]:.3f}")
```
"""
if quantiles is None:
quantiles = np.arange(1, 10) / 10
Expand Down
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