import numpy as np
from typing import Tuple
from .stratified import (
SimpleStratifiedDistributionEstimator,
AdjustedStratifiedDistributionEstimator,
)
from .util import compute_ldte, compute_lpte
[docs]
class SimpleLocalDistributionEstimator(SimpleStratifiedDistributionEstimator):
"""
A class for computing Local Distribution Treatment Effects (LDTE) and Local Probability
Treatment Effects (LPTE) using simple empirical estimation.
This estimator computes treatment effects that are weighted by treatment propensity
within each stratum, providing estimates that are locally robust to treatment assignment
heterogeneity across strata. It uses empirical methods without ML adjustment.
"""
def __init__(self):
"""
Initializes the SimpleLocalDistributionEstimator.
Returns:
SimpleLocalDistributionEstimator: An instance of the estimator.
"""
super().__init__()
[docs]
def fit(
self,
covariates: np.ndarray,
treatment_arms: np.ndarray,
treatment_indicator: np.ndarray,
outcomes: np.ndarray,
strata: np.ndarray,
) -> "SimpleLocalDistributionEstimator":
"""
Train the SimpleLocalDistributionEstimator.
Args:
covariates (np.ndarray): Pre-treatment covariates.
treatment_arms (np.ndarray): Treatment assignment variable (Z).
treatment_indicator (np.ndarray): Treatment indicator variable (D).
outcomes (np.ndarray): Scalar-valued observed outcome.
strata (np.ndarray): Stratum indicators.
Returns:
SimpleLocalDistributionEstimator: The fitted estimator.
"""
super().fit(covariates, treatment_arms, outcomes, strata)
self.treatment_indicator = treatment_indicator
return self
[docs]
def predict_ldte(
self,
target_treatment_arm: int,
control_treatment_arm: int,
locations: np.ndarray,
alpha: float = 0.05,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""
Compute Local Distribution Treatment Effects (LDTE).
LDTE measures the difference in cumulative distribution functions between treatment groups
weighted by treatment propensity within each stratum. This provides estimates that are
locally robust to treatment assignment heterogeneity across strata.
Args:
target_treatment_arm (int): The index of the treatment arm of the treatment group.
control_treatment_arm (int): The index of the treatment arm of the control group.
locations (np.ndarray): Scalar values to be used for computing the cumulative distribution.
alpha (float, optional): Significance level of the confidence bound. Defaults to 0.05.
Returns:
Tuple[np.ndarray, np.ndarray, np.ndarray]: A tuple containing:
- Expected LDTEs (np.ndarray): Local treatment effect estimates at each location
- Lower bounds (np.ndarray): Lower confidence interval bounds
- Upper bounds (np.ndarray): Upper confidence interval bounds
Example:
.. code-block:: python
import numpy as np
from sklearn.linear_model import LogisticRegression
from dte_adj import AdjustedLocalDistributionEstimator
# Generate sample data with strata
np.random.seed(42)
X = np.random.randn(1000, 5)
strata = np.random.choice([0, 1], size=1000) # Binary strata
D = np.random.binomial(1, 0.3 + 0.4 * strata, 1000) # Treatment depends on strata
Y = X[:, 0] + 2 * D + strata + np.random.randn(1000)
# Fit local estimator
base_model = LogisticRegression()
estimator = AdjustedLocalDistributionEstimator(base_model)
estimator.fit(X, D, D, Y, strata) # treatment_arms = treatment_indicator for binary case
# Compute LDTE
locations = np.linspace(Y.min(), Y.max(), 20)
ldte, lower, upper = estimator.predict_ldte(
target_treatment_arm=1,
control_treatment_arm=0,
locations=locations
)
print(f"LDTE shape: {ldte.shape}") # Should match locations.shape
print(f"Average LDTE: {ldte.mean():.3f}")
"""
return compute_ldte(
self,
target_treatment_arm,
control_treatment_arm,
locations,
alpha,
)
[docs]
def predict_lpte(
self,
target_treatment_arm: int,
control_treatment_arm: int,
locations: np.ndarray,
alpha: float = 0.05,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""
Compute Local Probability Treatment Effects (LPTE).
LPTE measures the difference in probability mass between treatment groups for intervals
defined by consecutive location pairs, weighted by treatment propensity within each stratum.
This provides locally robust estimates of treatment effects on interval probabilities.
Args:
target_treatment_arm (int): The index of the treatment arm of the treatment group.
control_treatment_arm (int): The index of the treatment arm of the control group.
locations (np.ndarray): Scalar values defining interval boundaries for probability computation.
For each interval (locations[i], locations[i+1]], the LPTE is computed.
alpha (float, optional): Significance level of the confidence bound. Defaults to 0.05.
Returns:
Tuple[np.ndarray, np.ndarray, np.ndarray]: A tuple containing:
- Expected LPTEs (np.ndarray): Local treatment effect estimates for each interval,
shape (len(locations)-1,)
- Lower bounds (np.ndarray): Lower confidence interval bounds
- Upper bounds (np.ndarray): Upper confidence interval bounds
Example:
.. code-block:: python
import numpy as np
from dte_adj import SimpleLocalDistributionEstimator
# Generate sample data with strata
np.random.seed(42)
X = np.random.randn(1000, 5)
strata = np.random.choice([0, 1], size=1000) # Binary strata
Z = np.random.binomial(1, 0.5, 1000) # Treatment assignment
D = np.random.binomial(1, 0.3 + 0.4 * Z, 1000) # Treatment receipt
Y = X[:, 0] + 2 * D + strata + np.random.randn(1000)
# Fit local estimator
estimator = SimpleLocalDistributionEstimator()
estimator.fit(X, Z, D, Y, strata)
# Define interval boundaries
locations = np.array([-2, -1, 0, 1, 2]) # Creates intervals: (-2,-1], (-1,0], (0,1], (1,2]
# Compute LPTE
lpte, lower, upper = estimator.predict_lpte(
target_treatment_arm=1,
control_treatment_arm=0,
locations=locations
)
print(f"LPTE shape: {lpte.shape}") # Should be (4,) for 4 intervals
print(f"Interval effects: {lpte}")
"""
return compute_lpte(
self,
target_treatment_arm,
control_treatment_arm,
locations,
alpha,
)
[docs]
class AdjustedLocalDistributionEstimator(AdjustedStratifiedDistributionEstimator):
"""
A class for computing Local Distribution Treatment Effects (LDTE) and Local Probability
Treatment Effects (LPTE) using machine learning adjustment.
This estimator combines the benefits of ML adjustment with local treatment effect estimation,
providing precise estimates of treatment effects that are weighted by treatment propensity
within each stratum. It uses cross-fitting to avoid overfitting issues.
"""
[docs]
def fit(
self,
covariates: np.ndarray,
treatment_arms: np.ndarray,
treatment_indicator: np.ndarray,
outcomes: np.ndarray,
strata: np.ndarray,
) -> "AdjustedLocalDistributionEstimator":
"""
Train the AdjustedLocalDistributionEstimator.
Args:
covariates (np.ndarray): Pre-treatment covariates.
treatment_arms (np.ndarray): Treatment assignment variable (Z).
treatment_indicator (np.ndarray): Treatment indicator variable (D).
outcomes (np.ndarray): Scalar-valued observed outcome.
strata (np.ndarray): Stratum indicators.
Returns:
AdjustedLocalDistributionEstimator: The fitted estimator.
"""
super().fit(covariates, treatment_arms, outcomes, strata)
self.treatment_indicator = treatment_indicator
return self
[docs]
def predict_ldte(
self,
target_treatment_arm: int,
control_treatment_arm: int,
locations: np.ndarray,
alpha: float = 0.05,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""
Compute Local Distribution Treatment Effects (LDTE) using ML adjustment.
This method combines machine learning adjustment with local treatment effect estimation
to provide precise, locally robust estimates of distributional treatment effects.
Args:
target_treatment_arm (int): The index of the treatment arm of the treatment group.
control_treatment_arm (int): The index of the treatment arm of the control group.
locations (np.ndarray): Scalar values to be used for computing the cumulative distribution.
alpha (float, optional): Significance level of the confidence bound. Defaults to 0.05.
Returns:
Tuple[np.ndarray, np.ndarray, np.ndarray]: A tuple containing:
- Expected LDTEs (np.ndarray): Local treatment effect estimates at each location
- Lower bounds (np.ndarray): Lower confidence interval bounds
- Upper bounds (np.ndarray): Upper confidence interval bounds
Example:
.. code-block:: python
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from dte_adj import AdjustedLocalDistributionEstimator
# Generate confounded data with strata
np.random.seed(42)
X = np.random.randn(1000, 5)
strata = np.random.choice([0, 1], size=1000)
# Treatment assignment depends on covariates
Z_prob = 1 / (1 + np.exp(-(X[:, 0] + X[:, 1] + strata)))
Z = np.random.binomial(1, Z_prob, 1000)
D = np.random.binomial(1, 0.3 + 0.4 * Z, 1000)
Y = X.sum(axis=1) + 2 * D + strata + np.random.randn(1000)
# Fit adjusted local estimator
base_model = RandomForestClassifier(n_estimators=100)
estimator = AdjustedLocalDistributionEstimator(base_model, folds=3)
estimator.fit(X, Z, D, Y, strata)
# Compute LDTE with ML adjustment
locations = np.linspace(Y.min(), Y.max(), 20)
ldte, lower, upper = estimator.predict_ldte(
target_treatment_arm=1,
control_treatment_arm=0,
locations=locations
)
print(f"Adjusted LDTE: {ldte.mean():.3f}")
"""
return compute_ldte(
self,
target_treatment_arm,
control_treatment_arm,
locations,
alpha,
)
[docs]
def predict_lpte(
self,
target_treatment_arm: int,
control_treatment_arm: int,
locations: np.ndarray,
alpha: float = 0.05,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""
Compute Local Probability Treatment Effects (LPTE) using ML adjustment.
This method combines machine learning adjustment with local treatment effect estimation
to provide precise estimates of treatment effects on interval probabilities.
Args:
target_treatment_arm (int): The index of the treatment arm of the treatment group.
control_treatment_arm (int): The index of the treatment arm of the control group.
locations (np.ndarray): Scalar values defining interval boundaries for probability computation.
For each interval (locations[i], locations[i+1]], the LPTE is computed.
alpha (float, optional): Significance level of the confidence bound. Defaults to 0.05.
Returns:
Tuple[np.ndarray, np.ndarray, np.ndarray]: A tuple containing:
- Expected LPTEs (np.ndarray): Local treatment effect estimates for each interval,
shape (len(locations)-1,)
- Lower bounds (np.ndarray): Lower confidence interval bounds
- Upper bounds (np.ndarray): Upper confidence interval bounds
Example:
.. code-block:: python
import numpy as np
from sklearn.linear_model import LogisticRegression
from dte_adj import AdjustedLocalDistributionEstimator
# Generate confounded data with strata
np.random.seed(42)
X = np.random.randn(1000, 5)
strata = np.random.choice([0, 1], size=1000)
# Treatment assignment depends on covariates
Z_prob = 1 / (1 + np.exp(-(X[:, 0] + strata)))
Z = np.random.binomial(1, Z_prob, 1000)
D = np.random.binomial(1, 0.3 + 0.4 * Z, 1000)
Y = X.sum(axis=1) + 2 * D + strata + np.random.randn(1000)
# Fit adjusted local estimator
base_model = LogisticRegression()
estimator = AdjustedLocalDistributionEstimator(base_model, folds=3)
estimator.fit(X, Z, D, Y, strata)
# Define interval boundaries
locations = np.array([-2, -1, 0, 1, 2])
# Compute LPTE with ML adjustment
lpte, lower, upper = estimator.predict_lpte(
target_treatment_arm=1,
control_treatment_arm=0,
locations=locations
)
print(f"Adjusted LPTE: {lpte}")
"""
return compute_lpte(
self,
target_treatment_arm,
control_treatment_arm,
locations,
alpha,
)