import copy
from copy import deepcopy
import logging
import numpy as np
from packaging import version
from scipy.stats import norm
import sklearn
from sklearn.exceptions import ConvergenceWarning
from sklearn.neural_network import MLPRegressor
if version.parse(sklearn.__version__) >= version.parse("0.22.0"):
from sklearn.utils._testing import ignore_warnings
else:
from sklearn.utils.testing import ignore_warnings
from tqdm import tqdm
from xgboost import XGBRegressor
from causalml.inference.meta.base import BaseLearner
from causalml.inference.meta.utils import check_treatment_vector, convert_pd_to_np
from causalml.metrics import regression_metrics, classification_metrics
logger = logging.getLogger("causalml")
[docs]
class BaseTLearner(BaseLearner):
"""A parent class for T-learner regressor classes.
A T-learner estimates treatment effects with two machine learning models.
Details of T-learner are available at `Kunzel et al. (2018) <https://arxiv.org/abs/1706.03461>`_.
"""
def __init__(
self,
learner=None,
control_learner=None,
treatment_learner=None,
ate_alpha=0.05,
control_name=0,
):
"""Initialize a T-learner.
Args:
learner (model): a model to estimate control and treatment outcomes.
control_learner (model, optional): a model to estimate control outcomes
treatment_learner (model, optional): a model to estimate treatment outcomes
ate_alpha (float, optional): the confidence level alpha of the ATE estimate
control_name (str or int, optional): name of control group
"""
assert (learner is not None) or (
(control_learner is not None) and (treatment_learner is not None)
)
if control_learner is None:
self.model_c = deepcopy(learner)
else:
self.model_c = control_learner
# Preserve the unfitted template so repeated fit() calls always start fresh.
self._model_c_template = self.model_c
if treatment_learner is None:
self.model_t = deepcopy(learner)
else:
self.model_t = treatment_learner
# Preserve the unfitted template so repeated fit() calls always start fresh.
self._model_t_template = self.model_t
self.ate_alpha = ate_alpha
self.control_name = control_name
self.bootstrap_models_ = None
def __repr__(self):
return "{}(model_c={}, model_t={})".format(
self.__class__.__name__, self.model_c.__repr__(), self.model_t.__repr__()
)
def _unfitted_clone(self):
template = copy.copy(self)
for attr in ("models_c", "models_t", "bootstrap_models_"):
if hasattr(template, attr):
delattr(template, attr)
template.model_c = self._model_c_template
template.model_t = self._model_t_template
return template
[docs]
@ignore_warnings(category=ConvergenceWarning)
def fit(
self,
X,
treatment,
y,
p=None,
store_bootstraps=False,
n_bootstraps=200,
bootstrap_size=10000,
random_state=None,
n_jobs=1,
):
"""Fit the inference model
Args:
X (np.matrix or np.array or pd.Dataframe): a feature matrix
treatment (np.array or pd.Series): a treatment vector
y (np.array or pd.Series): an outcome vector
p: unused, kept for API consistency
store_bootstraps (bool, optional): if True, trains a bootstrap ensemble
during fit and stores it in self.bootstrap_models_ for post-fit CI
estimation via predict(return_ci=True). Default: False.
n_bootstraps (int, optional): number of bootstrap iterations. Default: 200.
Note: storing N bootstraps of a GBM-based learner with k treatment
groups holds 2*N*k model objects in memory. Monitor RAM for large N
or heavy base learners.
n_jobs (int, optional): number of parallel jobs for bootstrap fitting.
-1 uses all available cores. Default: 1.
bootstrap_size (int, optional): number of samples per bootstrap. Default: 10000.
random_state (int, optional): random seed for reproducible bootstrap sampling.
"""
X, treatment, y = convert_pd_to_np(X, treatment, y)
check_treatment_vector(treatment, self.control_name)
self.t_groups = np.unique(treatment[treatment != self.control_name])
self.t_groups.sort()
self._classes = {group: i for i, group in enumerate(self.t_groups)}
self.models_t = {group: deepcopy(self.model_t) for group in self.t_groups}
# model_c is trained on the control group, which is identical for every
# treatment group, so fit it once. Deepcopy from the unfitted template so
# re-calling fit() always starts from a clean state (safe with warm_start).
control_mask = treatment == self.control_name
self.model_c = deepcopy(self._model_c_template)
self.model_c.fit(X[control_mask], y[control_mask])
# Expose as a shared-reference dict to preserve the public models_c API.
self.models_c = {group: self.model_c for group in self.t_groups}
for group in self.t_groups:
treatment_mask = treatment == group
self.models_t[group].fit(X[treatment_mask], y[treatment_mask])
if store_bootstraps:
self.fit_bootstrap_ensemble(
X=X,
treatment=treatment,
y=y,
n_bootstraps=n_bootstraps,
bootstrap_size=bootstrap_size,
random_state=random_state,
n_jobs=n_jobs,
)
else:
self.bootstrap_models_ = None
def _compute_bootstrap_ci(self, X):
"""Compute bootstrap CI using stored ensemble.
Args:
X (np.matrix or np.array or pd.Dataframe): a feature matrix
Returns:
(te_lower, te_upper): percentile CI bounds, each of shape [n_samples, n_treatment]
"""
if self.bootstrap_models_ is None:
raise ValueError(
"No bootstrap ensemble found. Call fit(..., store_bootstraps=True) first."
)
te_bootstraps = np.zeros(
(X.shape[0], self.t_groups.shape[0], len(self.bootstrap_models_))
)
for b, learner_b in enumerate(self.bootstrap_models_):
te_bootstraps[:, :, b] = learner_b.predict(X)
te_lower = np.percentile(te_bootstraps, (self.ate_alpha / 2) * 100, axis=2)
te_upper = np.percentile(te_bootstraps, (1 - self.ate_alpha / 2) * 100, axis=2)
return te_lower, te_upper
[docs]
def predict(
self,
X,
treatment=None,
y=None,
p=None,
return_components=False,
verbose=True,
return_ci=False,
):
"""Predict treatment effects.
Args:
X (np.matrix or np.array or pd.Dataframe): a feature matrix
treatment (np.array or pd.Series, optional): a treatment vector
y (np.array or pd.Series, optional): an outcome vector
return_components (bool, optional): whether to return outcome for
treatment and control separately
verbose (bool, optional): whether to output progress logs
return_ci (bool, optional): whether to return confidence intervals
using the stored bootstrap ensemble. Requires fit() to have been
called with store_bootstraps=True. CI width is controlled by
self.ate_alpha set at init time.
Returns:
(numpy.ndarray): Predictions of treatment effects. If return_ci=True,
returns (te, te_lower, te_upper) each of shape [n_samples, n_treatment].
return_ci=True and return_components=True cannot be used together.
"""
if return_ci and return_components:
raise ValueError("return_ci and return_components cannot both be True.")
X, treatment, y = convert_pd_to_np(X, treatment, y)
yhat_ts = {}
yhat_c = self.model_c.predict(X)
# Build a shared-reference dict so return_components callers keep the
# yhat_cs[group] indexing API without duplicating the underlying array.
yhat_cs = {group: yhat_c for group in self.t_groups}
for group in self.t_groups:
yhat_ts[group] = self.models_t[group].predict(X)
if (y is not None) and (treatment is not None) and verbose:
mask = (treatment == group) | (treatment == self.control_name)
treatment_filt = treatment[mask]
y_filt = y[mask]
w = (treatment_filt == group).astype(int)
yhat = np.zeros_like(y_filt, dtype=float)
yhat[w == 0] = yhat_c[mask][w == 0]
yhat[w == 1] = yhat_ts[group][mask][w == 1]
logger.info("Error metrics for group {}".format(group))
regression_metrics(y_filt, yhat, w)
te = np.zeros((X.shape[0], self.t_groups.shape[0]))
for i, group in enumerate(self.t_groups):
te[:, i] = yhat_ts[group] - yhat_c
if return_ci:
te_lower, te_upper = self._compute_bootstrap_ci(X)
return te, te_lower, te_upper
if not return_components:
return te
else:
return te, yhat_cs, yhat_ts
[docs]
def fit_predict(
self,
X,
treatment,
y,
p=None,
return_ci=False,
n_bootstraps=1000,
bootstrap_size=10000,
return_components=False,
verbose=True,
):
"""Fit the inference model of the T learner and predict treatment effects.
Args:
X (np.matrix or np.array or pd.Dataframe): a feature matrix
treatment (np.array or pd.Series): a treatment vector
y (np.array or pd.Series): an outcome vector
return_ci (bool): whether to return confidence intervals
n_bootstraps (int): number of bootstrap iterations
bootstrap_size (int): number of samples per bootstrap
return_components (bool, optional): whether to return outcome for treatment and control seperately
verbose (str): whether to output progress logs
Returns:
(numpy.ndarray): Predictions of treatment effects. Output dim: [n_samples, n_treatment].
If return_ci, returns CATE [n_samples, n_treatment], LB [n_samples, n_treatment],
UB [n_samples, n_treatment]
"""
X, treatment, y = convert_pd_to_np(X, treatment, y)
self.fit(X, treatment, y)
te = self.predict(X, treatment, y, return_components=return_components)
if not return_ci:
return te
else:
t_groups_global = self.t_groups
_classes_global = self._classes
model_c_global = deepcopy(self.model_c)
models_t_global = deepcopy(self.models_t)
te_bootstraps = np.zeros(
shape=(X.shape[0], self.t_groups.shape[0], n_bootstraps)
)
logger.info("Bootstrap Confidence Intervals")
for i in tqdm(range(n_bootstraps)):
te_b = self.bootstrap(X, treatment, y, size=bootstrap_size)
te_bootstraps[:, :, i] = te_b
te_lower = np.percentile(te_bootstraps, (self.ate_alpha / 2) * 100, axis=2)
te_upper = np.percentile(
te_bootstraps, (1 - self.ate_alpha / 2) * 100, axis=2
)
# set member variables back to global (currently last bootstrapped outcome)
self.t_groups = t_groups_global
self._classes = _classes_global
self.model_c = deepcopy(model_c_global)
self.models_c = {group: self.model_c for group in self.t_groups}
self.models_t = deepcopy(models_t_global)
return (te, te_lower, te_upper)
[docs]
def estimate_ate(
self,
X,
treatment,
y,
p=None,
bootstrap_ci=False,
n_bootstraps=1000,
bootstrap_size=10000,
pretrain=False,
):
"""Estimate the Average Treatment Effect (ATE).
Args:
X (np.matrix or np.array or pd.Dataframe): a feature matrix
treatment (np.array or pd.Series): a treatment vector
y (np.array or pd.Series): an outcome vector
bootstrap_ci (bool): whether to return confidence intervals
n_bootstraps (int): number of bootstrap iterations
bootstrap_size (int): number of samples per bootstrap
Returns:
The mean and confidence interval (LB, UB) of the ATE estimate.
pretrain (bool): whether a model has been fit, default False.
"""
X, treatment, y = convert_pd_to_np(X, treatment, y)
if pretrain:
te, yhat_cs, yhat_ts = self.predict(X, treatment, y, return_components=True)
else:
te, yhat_cs, yhat_ts = self.fit_predict(
X, treatment, y, return_components=True
)
ate = np.zeros(self.t_groups.shape[0])
ate_lb = np.zeros(self.t_groups.shape[0])
ate_ub = np.zeros(self.t_groups.shape[0])
for i, group in enumerate(self.t_groups):
_ate = te[:, i].mean()
mask = (treatment == group) | (treatment == self.control_name)
treatment_filt = treatment[mask]
y_filt = y[mask]
w = (treatment_filt == group).astype(int)
prob_treatment = float(sum(w)) / w.shape[0]
yhat_c = yhat_cs[group][mask]
yhat_t = yhat_ts[group][mask]
se = np.sqrt(
(
(y_filt[w == 0] - yhat_c[w == 0]).var() / (1 - prob_treatment)
+ (y_filt[w == 1] - yhat_t[w == 1]).var() / prob_treatment
+ (yhat_t - yhat_c).var()
)
/ y_filt.shape[0]
)
_ate_lb = _ate - se * norm.ppf(1 - self.ate_alpha / 2)
_ate_ub = _ate + se * norm.ppf(1 - self.ate_alpha / 2)
ate[i] = _ate
ate_lb[i] = _ate_lb
ate_ub[i] = _ate_ub
if not bootstrap_ci:
return ate, ate_lb, ate_ub
else:
t_groups_global = self.t_groups
_classes_global = self._classes
model_c_global = deepcopy(self.model_c)
models_t_global = deepcopy(self.models_t)
logger.info("Bootstrap Confidence Intervals for ATE")
ate_bootstraps = np.zeros(shape=(self.t_groups.shape[0], n_bootstraps))
for n in tqdm(range(n_bootstraps)):
ate_b = self.bootstrap(X, treatment, y, size=bootstrap_size)
ate_bootstraps[:, n] = ate_b.mean(axis=0)
ate_lower = np.percentile(
ate_bootstraps, (self.ate_alpha / 2) * 100, axis=1
)
ate_upper = np.percentile(
ate_bootstraps, (1 - self.ate_alpha / 2) * 100, axis=1
)
# set member variables back to global (currently last bootstrapped outcome)
self.t_groups = t_groups_global
self._classes = _classes_global
self.model_c = deepcopy(model_c_global)
self.models_c = {group: self.model_c for group in self.t_groups}
self.models_t = deepcopy(models_t_global)
return ate, ate_lower, ate_upper
[docs]
class BaseTRegressor(BaseTLearner):
"""
A parent class for T-learner regressor classes.
"""
def __init__(
self,
learner=None,
control_learner=None,
treatment_learner=None,
ate_alpha=0.05,
control_name=0,
):
"""Initialize a T-learner regressor.
Args:
learner (model): a model to estimate control and treatment outcomes.
control_learner (model, optional): a model to estimate control outcomes
treatment_learner (model, optional): a model to estimate treatment outcomes
ate_alpha (float, optional): the confidence level alpha of the ATE estimate
control_name (str or int, optional): name of control group
"""
super().__init__(
learner=learner,
control_learner=control_learner,
treatment_learner=treatment_learner,
ate_alpha=ate_alpha,
control_name=control_name,
)
[docs]
class BaseTClassifier(BaseTLearner):
"""
A parent class for T-learner classifier classes.
"""
def __init__(
self,
learner=None,
control_learner=None,
treatment_learner=None,
ate_alpha=0.05,
control_name=0,
):
"""Initialize a T-learner classifier.
Args:
learner (model): a model to estimate control and treatment outcomes.
control_learner (model, optional): a model to estimate control outcomes
treatment_learner (model, optional): a model to estimate treatment outcomes
ate_alpha (float, optional): the confidence level alpha of the ATE estimate
control_name (str or int, optional): name of control group
"""
super().__init__(
learner=learner,
control_learner=control_learner,
treatment_learner=treatment_learner,
ate_alpha=ate_alpha,
control_name=control_name,
)
[docs]
def predict(
self,
X,
treatment=None,
y=None,
p=None,
return_components=False,
verbose=True,
return_ci=False,
):
"""Predict treatment effects.
Args:
X (np.matrix or np.array or pd.Dataframe): a feature matrix
treatment (np.array or pd.Series, optional): a treatment vector
y (np.array or pd.Series, optional): an outcome vector
verbose (bool, optional): whether to output progress logs
Returns:
(numpy.ndarray): Predictions of treatment effects.
"""
if return_ci and return_components:
raise ValueError("return_ci and return_components cannot both be True.")
yhat_ts = {}
yhat_c = self.model_c.predict_proba(X)[:, 1]
yhat_cs = {group: yhat_c for group in self.t_groups}
for group in self.t_groups:
yhat_ts[group] = self.models_t[group].predict_proba(X)[:, 1]
if (y is not None) and (treatment is not None) and verbose:
mask = (treatment == group) | (treatment == self.control_name)
treatment_filt = treatment[mask]
y_filt = y[mask]
w = (treatment_filt == group).astype(int)
yhat = np.zeros_like(y_filt, dtype=float)
yhat[w == 0] = yhat_c[mask][w == 0]
yhat[w == 1] = yhat_ts[group][mask][w == 1]
logger.info("Error metrics for group {}".format(group))
classification_metrics(y_filt, yhat, w)
te = np.zeros((X.shape[0], self.t_groups.shape[0]))
for i, group in enumerate(self.t_groups):
te[:, i] = yhat_ts[group] - yhat_c
if return_ci:
te_lower, te_upper = self._compute_bootstrap_ci(X)
return te, te_lower, te_upper
if not return_components:
return te
else:
return te, yhat_cs, yhat_ts
[docs]
class XGBTRegressor(BaseTRegressor):
def __init__(self, ate_alpha=0.05, control_name=0, *args, **kwargs):
"""Initialize a T-learner with two XGBoost models."""
super().__init__(
learner=XGBRegressor(*args, **kwargs),
ate_alpha=ate_alpha,
control_name=control_name,
)
[docs]
class MLPTRegressor(BaseTRegressor):
def __init__(self, ate_alpha=0.05, control_name=0, *args, **kwargs):
"""Initialize a T-learner with two MLP models."""
super().__init__(
learner=MLPRegressor(*args, **kwargs),
ate_alpha=ate_alpha,
control_name=control_name,
)