econml.iv.dr.SparseLinearDRIV
- class econml.iv.dr.SparseLinearDRIV(*, model_y_xw='auto', model_t_xw='auto', model_z_xw='auto', model_t_xwz='auto', model_tz_xw='auto', flexible_model_effect='auto', prel_cate_approach='driv', prel_cv=1, prel_opt_reweighted=True, projection=False, featurizer=None, fit_cate_intercept=True, alpha='auto', n_alphas=100, alpha_cov='auto', n_alphas_cov=10, max_iter=1000, tol=0.0001, n_jobs=None, cov_clip=0.001, opt_reweighted=False, discrete_instrument=False, discrete_treatment=False, treatment_featurizer=None, categories='auto', cv=2, mc_iters=None, mc_agg='mean', random_state=None)[source]
Bases:
econml._cate_estimator.DebiasedLassoCateEstimatorMixin
,econml.iv.dr._dr.DRIV
Special case of the
DRIV
where the final stage is a Debiased Lasso Regression. In this case, inference can be performed via the debiased lasso approach and its asymptotic normal characterization of the estimated parameters. This is computationally faster than bootstrap inference. Leave the defaultinference='auto'
unchanged, or explicitly setinference='debiasedlasso'
at fit time to enable inference via asymptotic normality.- Parameters
model_y_xw (estimator or ‘auto’ (default is ‘auto’)) – model to estimate \(\E[Y | X, W]\). Must support fit and predict methods. If ‘auto’
WeightedLassoCV
/WeightedMultiTaskLassoCV
will be chosen.model_t_xw (estimator or ‘auto’ (default is ‘auto’)) – model to estimate \(\E[T | X, W]\). Must support fit and predict methods. If ‘auto’,
LogisticRegressionCV
will be applied for discrete treatment, andWeightedLassoCV
/WeightedMultiTaskLassoCV
will be applied for continuous treatment.model_z_xw (estimator or ‘auto’ (default is ‘auto’)) – model to estimate \(\E[Z | X, W]\). Must support fit and predict methods. If ‘auto’,
LogisticRegressionCV
will be applied for discrete instrument, andWeightedLassoCV
/WeightedMultiTaskLassoCV
will be applied for continuous instrument.model_t_xwz (estimator or ‘auto’ (default is ‘auto’)) – model to estimate \(\E[T | X, W, Z]\). Must support fit and predict methods. If ‘auto’,
LogisticRegressionCV
will be applied for discrete treatment, andWeightedLassoCV
/WeightedMultiTaskLassoCV
will be applied for continuous treatment.model_tz_xw (estimator or ‘auto’ (default is ‘auto’)) – model to estimate \(\E[T*Z | X, W]\). Must support fit and predict methods. If ‘auto’,
LogisticRegressionCV
will be applied for discrete instrument and discrete treatment, andWeightedLassoCV
/WeightedMultiTaskLassoCV
will be applied for continuous instrument or continuous treatment.flexible_model_effect (estimator or ‘auto’ (default is ‘auto’)) – a flexible model for a preliminary version of the CATE, must accept sample_weight at fit time. If ‘auto’,
StatsModelsLinearRegression
will be applied.prel_cate_approach (one of {‘driv’, ‘dmliv’}, default ‘driv’) – model that estimates a preliminary version of the CATE. If ‘driv’,
_DRIV
will be used. If ‘dmliv’,NonParamDMLIV
will be usedprel_cv (int, cross-validation generator or an iterable, default 1) – Determines the cross-validation splitting strategy for the preliminary effect model.
prel_opt_reweighted (bool, default True) – Whether to reweight the samples to minimize variance for the preliminary effect model.
projection (bool, default False) – If True, we fit a slight variant of DRIV where we use E[T|X, W, Z] as the instrument as opposed to Z, model_z_xw will be disabled; If False, model_t_xwz will be disabled.
featurizer (transformer, optional) – Must support fit_transform and transform. Used to create composite features in the final CATE regression. It is ignored if X is None. The final CATE will be trained on the outcome of featurizer.fit_transform(X). If featurizer=None, then CATE is trained on X.
fit_cate_intercept (bool, default True) – Whether the linear CATE model should have a constant term.
alpha (str | float, optional., default ‘auto’.) – CATE L1 regularization applied through the debiased lasso in the final model. ‘auto’ corresponds to a CV form of the
DebiasedLasso
.n_alphas (int, default 100) – How many alphas to try if alpha=’auto’
alpha_cov (str | float, default ‘auto’) – The regularization alpha that is used when constructing the pseudo inverse of the covariance matrix Theta used to for correcting the final state lasso coefficient in the debiased lasso. Each such regression corresponds to the regression of one feature on the remainder of the features.
n_alphas_cov (int, default 10) – How many alpha_cov to try if alpha_cov=’auto’.
max_iter (int, default 1000) – The maximum number of iterations in the Debiased Lasso
tol (float, default 1e-4) – The tolerance for the optimization: if the updates are smaller than
tol
, the optimization code checks the dual gap for optimality and continues until it is smaller thantol
.n_jobs (int or None, optional) – The number of jobs to run in parallel for both fit and predict.
None
means 1 unless in ajoblib.parallel_backend()
context.-1
means using all processors.cov_clip (float, default 0.1) – clipping of the covariate for regions with low “overlap”, to reduce variance
opt_reweighted (bool, default False) – Whether to reweight the samples to minimize variance. If True then model_final.fit must accept sample_weight as a kw argument. If True then assumes the model_final is flexible enough to fit the true CATE model. Otherwise, it method will return a biased projection to the model_final space, biased to give more weight on parts of the feature space where the instrument is strong.
discrete_instrument (bool, default False) – Whether the instrument values should be treated as categorical, rather than continuous, quantities
discrete_treatment (bool, default False) – Whether the treatment values should be treated as categorical, rather than continuous, quantities
treatment_featurizer (transformer, optional) – Must support fit_transform and transform. Used to create composite treatment in the final CATE regression. The final CATE will be trained on the outcome of featurizer.fit_transform(T). If featurizer=None, then CATE is trained on T.
categories (‘auto’ or list, default ‘auto’) – The categories to use when encoding discrete treatments (or ‘auto’ to use the unique sorted values). The first category will be treated as the control treatment.
cv (int, cross-validation generator or an iterable, default 2) – Determines the cross-validation splitting strategy. Possible inputs for cv are:
None, to use the default 3-fold cross-validation,
integer, to specify the number of folds.
An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if the treatment is discrete
StratifiedKFold
is used, else,KFold
is used (with a random shuffle in either case).Unless an iterable is used, we call split(concat[W, X], T) to generate the splits. If all W, X are None, then we call split(ones((T.shape[0], 1)), T).
mc_iters (int, optional) – The number of times to rerun the first stage models to reduce the variance of the nuisances.
mc_agg ({‘mean’, ‘median’}, default ‘mean’) – How to aggregate the nuisance value for each sample across the mc_iters monte carlo iterations of cross-fitting.
random_state (int, RandomState instance, or None, default None) – If int, random_state is the seed used by the random number generator; If
RandomState
instance, random_state is the random number generator; If None, the random number generator is theRandomState
instance used bynp.random
.
Examples
A simple example with the default models:
from econml.iv.dr import SparseLinearDRIV # Define the data generation functions def dgp(n, p, true_fn): X = np.random.normal(0, 1, size=(n, p)) Z = np.random.binomial(1, 0.5, size=(n,)) nu = np.random.uniform(0, 10, size=(n,)) coef_Z = 0.8 C = np.random.binomial( 1, coef_Z * scipy.special.expit(0.4 * X[:, 0] + nu) ) # Compliers when recomended C0 = np.random.binomial( 1, 0.06 * np.ones(X.shape[0]) ) # Non-compliers when not recommended T = C * Z + C0 * (1 - Z) y = true_fn(X) * T + 2 * nu + 5 * (X[:, 3] > 0) + 0.1 * np.random.uniform(0, 1, size=(n,)) return y, T, Z, X def true_heterogeneity_function(X): return 5 * X[:, 0] np.random.seed(123) y, T, Z, X = dgp(1000, 5, true_heterogeneity_function) est = SparseLinearDRIV(discrete_treatment=True, discrete_instrument=True) est.fit(Y=y, T=T, Z=Z, X=X)
>>> est.effect(X[:3]) array([-4.83304..., 5.76728..., -3.38677...]) >>> est.effect_interval(X[:3]) (array([-7.73949... , 1.62799..., -5.92335... ]), array([-1.92659..., 9.90657..., -0.85020...])) >>> est.coef_ array([ 4.91422... , 0.75957..., 0.23460..., -0.02084..., 0.08548...]) >>> est.coef__interval() (array([ 3.71131... , -0.45763..., -1.00739..., -1.20516..., -1.15926...]), array([6.11713..., 1.97678..., 1.47661..., 1.16347..., 1.33023...])) >>> est.intercept_ -0.30389... >>> est.intercept__interval() (-1.50685..., 0.89906...)
- __init__(*, model_y_xw='auto', model_t_xw='auto', model_z_xw='auto', model_t_xwz='auto', model_tz_xw='auto', flexible_model_effect='auto', prel_cate_approach='driv', prel_cv=1, prel_opt_reweighted=True, projection=False, featurizer=None, fit_cate_intercept=True, alpha='auto', n_alphas=100, alpha_cov='auto', n_alphas_cov=10, max_iter=1000, tol=0.0001, n_jobs=None, cov_clip=0.001, opt_reweighted=False, discrete_instrument=False, discrete_treatment=False, treatment_featurizer=None, categories='auto', cv=2, mc_iters=None, mc_agg='mean', random_state=None)[source]
Methods
__init__
(*[, model_y_xw, model_t_xw, ...])ate
([X, T0, T1])Calculate the average treatment effect \(E_X[\tau(X, T0, T1)]\).
ate_inference
([X, T0, T1])Inference results for the quantity \(E_X[\tau(X, T0, T1)]\) produced by the model.
ate_interval
([X, T0, T1, alpha])Confidence intervals for the quantity \(E_X[\tau(X, T0, T1)]\) produced by the model.
cate_feature_names
([feature_names])Get the output feature names.
cate_output_names
([output_names])Public interface for getting output names.
cate_treatment_names
([treatment_names])Get treatment names.
The inference of coefficients in the linear model of the constant marginal treatment effect.
coef__interval
(*[, alpha])The coefficients in the linear model of the constant marginal treatment effect.
const_marginal_ate
([X])Calculate the average constant marginal CATE \(E_X[\theta(X)]\).
Inference results for the quantities \(E_X[\theta(X)]\) produced by the model.
const_marginal_ate_interval
([X, alpha])Confidence intervals for the quantities \(E_X[\theta(X)]\) produced by the model.
Calculate the constant marginal CATE \(\theta(·)\).
Inference results for the quantities \(\theta(X)\) produced by the model.
const_marginal_effect_interval
([X, alpha])Confidence intervals for the quantities \(\theta(X)\) produced by the model.
effect
([X, T0, T1])Calculate the heterogeneous treatment effect \(\tau(X, T0, T1)\).
effect_inference
([X, T0, T1])Inference results for the quantities \(\tau(X, T0, T1)\) produced by the model.
effect_interval
([X, T0, T1, alpha])Confidence intervals for the quantities \(\tau(X, T0, T1)\) produced by the model.
fit
(Y, T, *, Z[, X, W, sample_weight, ...])Estimate the counterfactual model from data, i.e. estimates function \(\theta(\cdot)\).
The inference of intercept in the linear model of the constant marginal treatment effect.
intercept__interval
(*[, alpha])The intercept in the linear model of the constant marginal treatment effect.
marginal_ate
(T[, X])Calculate the average marginal effect \(E_{T, X}[\partial\tau(T, X)]\).
marginal_ate_inference
(T[, X])Inference results for the quantities \(E_{T,X}[\partial \tau(T, X)]\) produced by the model.
marginal_ate_interval
(T[, X, alpha])Confidence intervals for the quantities \(E_{T,X}[\partial \tau(T, X)]\) produced by the model.
marginal_effect
(T[, X])Calculate the heterogeneous marginal effect \(\partial\tau(T, X)\).
marginal_effect_inference
(T[, X])Inference results for the quantities \(\partial \tau(T, X)\) produced by the model.
marginal_effect_interval
(T[, X, alpha])Confidence intervals for the quantities \(\partial \tau(T, X)\) produced by the model.
refit_final
(*[, inference])Estimate the counterfactual model using a new final model specification but with cached first stage results.
score
(Y, T, Z[, X, W, sample_weight])Score the fitted CATE model on a new data set.
shap_values
(X, *[, feature_names, ...])Shap value for the final stage models (const_marginal_effect)
summary
([alpha, value, decimals, ...])The summary of coefficient and intercept in the linear model of the constant marginal treatment effect.
Attributes
bias_part_of_coef
The coefficients in the linear model of the constant marginal treatment effect.
Get an instance of
DoWhyWrapper
to allow other functionalities from dowhy package.featurizer
Get the fitted featurizer.
fit_cate_intercept_
The intercept in the linear model of the constant marginal treatment effect.
Get the fitted final CATE model.
model_final
model_final_
models_nuisance_
Get the fitted preliminary CATE estimator.
Get the fitted models for \(\E[T | X]\).
Get the fitted models for \(\E[Z | X]\).
Get the fitted models for \(\E[T*Z | X]\).
Get the fitted models for \(\E[Y | X]\).
Get the fitted models for \(\E[Z | X]\).
Get the scores for prel_model_effect model on the out-of-sample training data
Get the scores for t_xw model on the out-of-sample training data
Get the scores for z_xw model on the out-of-sample training data
Get the scores for tz_xw model on the out-of-sample training data
Get the scores for y_xw model on the out-of-sample training data
Get the scores for z_xw model on the out-of-sample training data
original_featurizer
ortho_learner_model_final_
A tuple (prel_theta, Y_res, T_res, Z_res, cov, X, W, Z), of the residuals from the first stage estimation along with the associated X, W and Z.
transformer
- ate(X=None, *, T0=0, T1=1)
Calculate the average treatment effect \(E_X[\tau(X, T0, T1)]\).
The effect is calculated between the two treatment points and is averaged over the population of X variables.
- Parameters
T0 ((m, d_t) matrix or vector of length m) – Base treatments for each sample
T1 ((m, d_t) matrix or vector of length m) – Target treatments for each sample
X ((m, d_x) matrix, optional) – Features for each sample
- Returns
τ – Average treatment effects on each outcome Note that when Y is a vector rather than a 2-dimensional array, the result will be a scalar
- Return type
float or (d_y,) array
- ate_inference(X=None, *, T0=0, T1=1)
Inference results for the quantity \(E_X[\tau(X, T0, T1)]\) produced by the model. Available only when
inference
is notNone
, when calling the fit method.- Parameters
X ((m, d_x) matrix, optional) – Features for each sample
T0 ((m, d_t) matrix or vector of length m, default 0) – Base treatments for each sample
T1 ((m, d_t) matrix or vector of length m, default 1) – Target treatments for each sample
- Returns
PopulationSummaryResults – The inference results instance contains prediction and prediction standard error and can on demand calculate confidence interval, z statistic and p value. It can also output a dataframe summary of these inference results.
- Return type
- ate_interval(X=None, *, T0=0, T1=1, alpha=0.05)
Confidence intervals for the quantity \(E_X[\tau(X, T0, T1)]\) produced by the model. Available only when
inference
is notNone
, when calling the fit method.- Parameters
X ((m, d_x) matrix, optional) – Features for each sample
T0 ((m, d_t) matrix or vector of length m, default 0) – Base treatments for each sample
T1 ((m, d_t) matrix or vector of length m, default 1) – Target treatments for each sample
alpha (float in [0, 1], default 0.05) – The overall level of confidence of the reported interval. The alpha/2, 1-alpha/2 confidence interval is reported.
- Returns
lower, upper – The lower and the upper bounds of the confidence interval for each quantity.
- Return type
tuple(type of
ate(X, T0, T1)
, type ofate(X, T0, T1))
)
- cate_feature_names(feature_names=None)
Get the output feature names.
- Parameters
feature_names (list of str of length X.shape[1] or None) – The names of the input features. If None and X is a dataframe, it defaults to the column names from the dataframe.
- Returns
out_feature_names – The names of the output features \(\phi(X)\), i.e. the features with respect to which the final CATE model for each treatment is linear. It is the names of the features that are associated with each entry of the
coef_()
parameter. Available only when the featurizer is not None and has a method: get_feature_names(feature_names). Otherwise None is returned.- Return type
list of str or None
- cate_output_names(output_names=None)
Public interface for getting output names.
To be overriden by estimators that apply transformations the outputs.
- Parameters
output_names (list of str of length Y.shape[1] or None) – The names of the outcomes. If None and the Y passed to fit was a dataframe, it defaults to the column names from the dataframe.
- Returns
output_names – Returns output names.
- Return type
list of str
- cate_treatment_names(treatment_names=None)
Get treatment names.
If the treatment is discrete or featurized, it will return expanded treatment names.
- Parameters
treatment_names (list of str of length T.shape[1], optional) – The names of the treatments. If None and the T passed to fit was a dataframe, it defaults to the column names from the dataframe.
- Returns
out_treatment_names – Returns (possibly expanded) treatment names.
- Return type
list of str
- coef__inference()
The inference of coefficients in the linear model of the constant marginal treatment effect.
- Returns
InferenceResults – The inference of the coefficients in the final linear model
- Return type
- coef__interval(*, alpha=0.05)
The coefficients in the linear model of the constant marginal treatment effect.
- Parameters
alpha (float in [0, 1], default 0.05) – The overall level of confidence of the reported interval. The alpha/2, 1-alpha/2 confidence interval is reported.
- Returns
lb, ub – The lower and upper bounds of the confidence interval for each quantity.
- Return type
- const_marginal_ate(X=None)
Calculate the average constant marginal CATE \(E_X[\theta(X)]\).
- Parameters
X ((m, d_x) matrix, optional) – Features for each sample.
- Returns
theta – Average constant marginal CATE of each treatment on each outcome. Note that when Y or featurized-T (or T if treatment_featurizer is None) is a vector rather than a 2-dimensional array, the corresponding singleton dimensions in the output will be collapsed (e.g. if both are vectors, then the output of this method will also be a scalar)
- Return type
(d_y, d_f_t) matrix where d_f_t is the dimension of the featurized treatment. If treatment_featurizer is None, d_f_t = d_t.
- const_marginal_ate_inference(X=None)
Inference results for the quantities \(E_X[\theta(X)]\) produced by the model. Available only when
inference
is notNone
, when calling the fit method.- Parameters
X ((m, d_x) matrix, optional) – Features for each sample
- Returns
PopulationSummaryResults – The inference results instance contains prediction and prediction standard error and can on demand calculate confidence interval, z statistic and p value. It can also output a dataframe summary of these inference results.
- Return type
- const_marginal_ate_interval(X=None, *, alpha=0.05)
Confidence intervals for the quantities \(E_X[\theta(X)]\) produced by the model. Available only when
inference
is notNone
, when calling the fit method.- Parameters
X ((m, d_x) matrix, optional) – Features for each sample
alpha (float in [0, 1], default 0.05) – The overall level of confidence of the reported interval. The alpha/2, 1-alpha/2 confidence interval is reported.
- Returns
lower, upper – The lower and the upper bounds of the confidence interval for each quantity.
- Return type
tuple(type of
const_marginal_ate(X)
, type ofconst_marginal_ate(X)
)
- const_marginal_effect(X=None)
Calculate the constant marginal CATE \(\theta(·)\).
The marginal effect is conditional on a vector of features on a set of m test samples X[i].
- Parameters
X ((m, d_x) matrix, optional) – Features for each sample.
- Returns
theta – Constant marginal CATE of each featurized treatment on each outcome for each sample X[i]. Note that when Y or featurized-T (or T if treatment_featurizer is None) is a vector rather than a 2-dimensional array, the corresponding singleton dimensions in the output will be collapsed (e.g. if both are vectors, then the output of this method will also be a vector)
- Return type
(m, d_y, d_f_t) matrix or (d_y, d_f_t) matrix if X is None where d_f_t is the dimension of the featurized treatment. If treatment_featurizer is None, d_f_t = d_t.
- const_marginal_effect_inference(X=None)
Inference results for the quantities \(\theta(X)\) produced by the model. Available only when
inference
is notNone
, when calling the fit method.- Parameters
X ((m, d_x) matrix, optional) – Features for each sample
- Returns
InferenceResults – The inference results instance contains prediction and prediction standard error and can on demand calculate confidence interval, z statistic and p value. It can also output a dataframe summary of these inference results.
- Return type
- const_marginal_effect_interval(X=None, *, alpha=0.05)
Confidence intervals for the quantities \(\theta(X)\) produced by the model. Available only when
inference
is notNone
, when calling the fit method.- Parameters
X ((m, d_x) matrix, optional) – Features for each sample
alpha (float in [0, 1], default 0.05) – The overall level of confidence of the reported interval. The alpha/2, 1-alpha/2 confidence interval is reported.
- Returns
lower, upper – The lower and the upper bounds of the confidence interval for each quantity.
- Return type
tuple(type of
const_marginal_effect(X)
, type ofconst_marginal_effect(X)
)
- effect(X=None, *, T0=0, T1=1)
Calculate the heterogeneous treatment effect \(\tau(X, T0, T1)\).
The effect is calculated between the two treatment points conditional on a vector of features on a set of m test samples \(\{T0_i, T1_i, X_i\}\).
- Parameters
T0 ((m, d_t) matrix or vector of length m) – Base treatments for each sample
T1 ((m, d_t) matrix or vector of length m) – Target treatments for each sample
X ((m, d_x) matrix, optional) – Features for each sample
- Returns
τ – Heterogeneous treatment effects on each outcome for each sample Note that when Y is a vector rather than a 2-dimensional array, the corresponding singleton dimension will be collapsed (so this method will return a vector)
- Return type
(m, d_y) matrix
- effect_inference(X=None, *, T0=0, T1=1)
Inference results for the quantities \(\tau(X, T0, T1)\) produced by the model. Available only when
inference
is notNone
, when calling the fit method.- Parameters
X ((m, d_x) matrix, optional) – Features for each sample
T0 ((m, d_t) matrix or vector of length m, default 0) – Base treatments for each sample
T1 ((m, d_t) matrix or vector of length m, default 1) – Target treatments for each sample
- Returns
InferenceResults – The inference results instance contains prediction and prediction standard error and can on demand calculate confidence interval, z statistic and p value. It can also output a dataframe summary of these inference results.
- Return type
- effect_interval(X=None, *, T0=0, T1=1, alpha=0.05)
Confidence intervals for the quantities \(\tau(X, T0, T1)\) produced by the model. Available only when
inference
is notNone
, when calling the fit method.- Parameters
X ((m, d_x) matrix, optional) – Features for each sample
T0 ((m, d_t) matrix or vector of length m, default 0) – Base treatments for each sample
T1 ((m, d_t) matrix or vector of length m, default 1) – Target treatments for each sample
alpha (float in [0, 1], default 0.05) – The overall level of confidence of the reported interval. The alpha/2, 1-alpha/2 confidence interval is reported.
- Returns
lower, upper – The lower and the upper bounds of the confidence interval for each quantity.
- Return type
tuple(type of
effect(X, T0, T1)
, type ofeffect(X, T0, T1))
)
- fit(Y, T, *, Z, X=None, W=None, sample_weight=None, groups=None, cache_values=False, inference='auto')[source]
Estimate the counterfactual model from data, i.e. estimates function \(\theta(\cdot)\).
- Parameters
Y ((n,) vector of length n) – Outcomes for each sample
T ((n,) vector of length n) – Treatments for each sample
Z ((n, d_z) matrix) – Instruments for each sample
X ((n, d_x) matrix, optional) – Features for each sample
W ((n, d_w) matrix, optional) – Controls for each sample
sample_weight ((n,) array_like, optional) – Individual weights for each sample. If None, it assumes equal weight.
groups ((n,) vector, optional) – All rows corresponding to the same group will be kept together during splitting. If groups is not None, the cv argument passed to this class’s initializer must support a ‘groups’ argument to its split method.
cache_values (bool, default False) – Whether to cache inputs and first stage results, which will allow refitting a different final model
inference (str,
Inference
instance, or None) – Method for performing inference. This estimator supports'bootstrap'
(or an instance ofBootstrapInference
) and'debiasedlasso'
(or an instance ofLinearModelInferenceDiscrete
).
- Return type
self
- intercept__inference()
The inference of intercept in the linear model of the constant marginal treatment effect.
- Returns
InferenceResults – The inference of the intercept in the final linear model
- Return type
- intercept__interval(*, alpha=0.05)
The intercept in the linear model of the constant marginal treatment effect.
- Parameters
alpha (float in [0, 1], default 0.05) – The overall level of confidence of the reported interval. The alpha/2, 1-alpha/2 confidence interval is reported.
- Returns
lower, upper – The lower and upper bounds of the confidence interval.
- Return type
tuple(type of
intercept_()
, type ofintercept_()
)
- marginal_ate(T, X=None)
Calculate the average marginal effect \(E_{T, X}[\partial\tau(T, X)]\).
The marginal effect is calculated around a base treatment point and averaged over the population of X.
- Parameters
T ((m, d_t) matrix) – Base treatments for each sample
X ((m, d_x) matrix, optional) – Features for each sample
- Returns
grad_tau – Average marginal effects on each outcome Note that when Y or T is a vector rather than a 2-dimensional array, the corresponding singleton dimensions in the output will be collapsed (e.g. if both are vectors, then the output of this method will be a scalar)
- Return type
(d_y, d_t) array
- marginal_ate_inference(T, X=None)
Inference results for the quantities \(E_{T,X}[\partial \tau(T, X)]\) produced by the model. Available only when
inference
is notNone
, when calling the fit method.- Parameters
T ((m, d_t) matrix) – Base treatments for each sample
X ((m, d_x) matrix, optional) – Features for each sample
- Returns
PopulationSummaryResults – The inference results instance contains prediction and prediction standard error and can on demand calculate confidence interval, z statistic and p value. It can also output a dataframe summary of these inference results.
- Return type
- marginal_ate_interval(T, X=None, *, alpha=0.05)
Confidence intervals for the quantities \(E_{T,X}[\partial \tau(T, X)]\) produced by the model. Available only when
inference
is notNone
, when calling the fit method.- Parameters
T ((m, d_t) matrix) – Base treatments for each sample
X ((m, d_x) matrix, optional) – Features for each sample
alpha (float in [0, 1], default 0.05) – The overall level of confidence of the reported interval. The alpha/2, 1-alpha/2 confidence interval is reported.
- Returns
lower, upper – The lower and the upper bounds of the confidence interval for each quantity.
- Return type
tuple(type of
marginal_ate(T, X)
, type ofmarginal_ate(T, X)
)
- marginal_effect(T, X=None)
Calculate the heterogeneous marginal effect \(\partial\tau(T, X)\).
The marginal effect is calculated around a base treatment point conditional on a vector of features on a set of m test samples \(\{T_i, X_i\}\). If treatment_featurizer is None, the base treatment is ignored in this calculation and the result is equivalent to const_marginal_effect.
- Parameters
T ((m, d_t) matrix) – Base treatments for each sample
X ((m, d_x) matrix, optional) – Features for each sample
- Returns
grad_tau – Heterogeneous marginal effects on each outcome for each sample Note that when Y or T is a vector rather than a 2-dimensional array, the corresponding singleton dimensions in the output will be collapsed (e.g. if both are vectors, then the output of this method will also be a vector)
- Return type
(m, d_y, d_t) array
- marginal_effect_inference(T, X=None)
Inference results for the quantities \(\partial \tau(T, X)\) produced by the model. Available only when
inference
is notNone
, when calling the fit method.- Parameters
T ((m, d_t) matrix) – Base treatments for each sample
X ((m, d_x) matrix, optional) – Features for each sample
- Returns
InferenceResults – The inference results instance contains prediction and prediction standard error and can on demand calculate confidence interval, z statistic and p value. It can also output a dataframe summary of these inference results.
- Return type
- marginal_effect_interval(T, X=None, *, alpha=0.05)
Confidence intervals for the quantities \(\partial \tau(T, X)\) produced by the model. Available only when
inference
is notNone
, when calling the fit method.- Parameters
T ((m, d_t) matrix) – Base treatments for each sample
X ((m, d_x) matrix, optional) – Features for each sample
alpha (float in [0, 1], default 0.05) – The overall level of confidence of the reported interval. The alpha/2, 1-alpha/2 confidence interval is reported.
- Returns
lower, upper – The lower and the upper bounds of the confidence interval for each quantity.
- Return type
tuple(type of
marginal_effect(T, X)
, type ofmarginal_effect(T, X)
)
- refit_final(*, inference='auto')
Estimate the counterfactual model using a new final model specification but with cached first stage results.
In order for this to succeed,
fit
must have been called withcache_values=True
. This call will only refit the final model. This call we use the current setting of any parameters that change the final stage estimation. If any parameters that change how the first stage nuisance estimates has also been changed then it will have no effect. You need to call fit again to change the first stage estimation results.- Parameters
inference (inference method, optional) – The string or object that represents the inference method
- Returns
self – This instance
- Return type
- score(Y, T, Z, X=None, W=None, sample_weight=None)
Score the fitted CATE model on a new data set. Generates nuisance parameters for the new data set based on the fitted residual nuisance models created at fit time. It uses the mean prediction of the models fitted by the different crossfit folds. Then calculates the MSE of the final residual Y on residual T regression.
If model_final does not have a score method, then it raises an
AttributeError
- Parameters
Y ((n, d_y) matrix or vector of length n) – Outcomes for each sample
T ((n, d_t) matrix or vector of length n) – Treatments for each sample
Z ((n, d_z) matrix) – Instruments for each sample
X ((n, d_x) matrix, optional) – Features for each sample
W ((n, d_w) matrix, optional) – Controls for each sample
sample_weight ((n,) vector, optional) – Weights for each samples
- Returns
score – The MSE of the final CATE model on the new data.
- Return type
- shap_values(X, *, feature_names=None, treatment_names=None, output_names=None, background_samples=100)
Shap value for the final stage models (const_marginal_effect)
- Parameters
X ((m, d_x) matrix) – Features for each sample. Should be in the same shape of fitted X in final stage.
feature_names (list of str of length X.shape[1], optional) – The names of input features.
treatment_names (list, optional) – The name of featurized treatment. In discrete treatment scenario, the name should not include the name of the baseline treatment (i.e. the control treatment, which by default is the alphabetically smaller)
output_names (list, optional) – The name of the outcome.
background_samples (int , default 100) – How many samples to use to compute the baseline effect. If None then all samples are used.
- Returns
shap_outs – A nested dictionary by using each output name (e.g. ‘Y0’, ‘Y1’, … when output_names=None) and each treatment name (e.g. ‘T0’, ‘T1’, … when treatment_names=None) as key and the shap_values explanation object as value. If the input data at fit time also contain metadata, (e.g. are pandas DataFrames), then the column metatdata for the treatments, outcomes and features are used instead of the above defaults (unless the user overrides with explicitly passing the corresponding names).
- Return type
nested dictionary of Explanation object
- summary(alpha=0.05, value=0, decimals=3, feature_names=None, treatment_names=None, output_names=None)
The summary of coefficient and intercept in the linear model of the constant marginal treatment effect.
- Parameters
alpha (float in [0, 1], default 0.05) – The overall level of confidence of the reported interval. The alpha/2, 1-alpha/2 confidence interval is reported.
value (float, default 0) – The mean value of the metric you’d like to test under null hypothesis.
decimals (int, default 3) – Number of decimal places to round each column to.
feature_names (list of str, optional) – The input of the feature names
treatment_names (list of str, optional) – The names of the treatments
output_names (list of str, optional) – The names of the outputs
- Returns
smry – this holds the summary tables and text, which can be printed or converted to various output formats.
- Return type
Summary instance
- property coef_
The coefficients in the linear model of the constant marginal treatment effect.
- Returns
coef – Where n_x is the number of features that enter the final model (either the dimension of X or the dimension of featurizer.fit_transform(X) if the CATE estimator has a featurizer.), n_t is the number of treatments, n_y is the number of outcomes. Dimensions are omitted if the original input was a vector and not a 2D array. For binary treatment the n_t dimension is also omitted.
- Return type
(n_x,) or (n_t, n_x) or (n_y, n_t, n_x) array_like
- property dowhy
Get an instance of
DoWhyWrapper
to allow other functionalities from dowhy package. (e.g. causal graph, refutation test, etc.)- Returns
DoWhyWrapper – An instance of
DoWhyWrapper
- Return type
instance
- property featurizer_
Get the fitted featurizer.
- Returns
featurizer – An instance of the fitted featurizer that was used to preprocess X in the final CATE model training. Available only when featurizer is not None and X is not None.
- Return type
object of type(featurizer)
- property intercept_
The intercept in the linear model of the constant marginal treatment effect.
- Returns
intercept – Where n_t is the number of treatments, n_y is the number of outcomes. Dimensions are omitted if the original input was a vector and not a 2D array. For binary treatment the n_t dimension is also omitted.
- Return type
float or (n_y,) or (n_y, n_t) array_like
- property model_cate
Get the fitted final CATE model.
- Returns
model_cate – An instance of the model_final object that was fitted after calling fit which corresponds to the constant marginal CATE model.
- Return type
object of type(model_final)
- property models_prel_model_effect
Get the fitted preliminary CATE estimator.
- Returns
prel_model_effect – A nested list of instances of the prel_model_effect object. Number of sublist equals to number of monte carlo iterations, each element in the sublist corresponds to a crossfitting fold and is the model instance that was fitted for that training fold.
- Return type
nested list of objects of type(prel_model_effect)
- property models_t_xw
Get the fitted models for \(\E[T | X]\).
- Returns
models_t_xw – A nested list of instances of the model_t_xw object. Number of sublist equals to number of monte carlo iterations, each element in the sublist corresponds to a crossfitting fold and is the model instance that was fitted for that training fold.
- Return type
nested list of objects of type(model_t_xw)
- property models_t_xwz
Get the fitted models for \(\E[Z | X]\).
- Returns
models_z_xw – A nested list of instances of the model_z_xw object. Number of sublist equals to number of monte carlo iterations, each element in the sublist corresponds to a crossfitting fold and is the model instance that was fitted for that training fold.
- Return type
nested list of objects of type(model_z_xw)
- property models_tz_xw
Get the fitted models for \(\E[T*Z | X]\).
- Returns
models_tz_xw – A nested list of instances of the model_tz_xw object. Number of sublist equals to number of monte carlo iterations, each element in the sublist corresponds to a crossfitting fold and is the model instance that was fitted for that training fold.
- Return type
nested list of objects of type(model_tz_xw)
- property models_y_xw
Get the fitted models for \(\E[Y | X]\).
- Returns
models_y_xw – A nested list of instances of the model_y_xw object. Number of sublist equals to number of monte carlo iterations, each element in the sublist corresponds to a crossfitting fold and is the model instance that was fitted for that training fold.
- Return type
nested list of objects of type(model_y_xw)
- property models_z_xw
Get the fitted models for \(\E[Z | X]\).
- Returns
models_z_xw – A nested list of instances of the model_z_xw object. Number of sublist equals to number of monte carlo iterations, each element in the sublist corresponds to a crossfitting fold and is the model instance that was fitted for that training fold.
- Return type
nested list of objects of type(model_z_xw)
- property nuisance_scores_prel_model_effect
Get the scores for prel_model_effect model on the out-of-sample training data
- property nuisance_scores_t_xw
Get the scores for t_xw model on the out-of-sample training data
- property nuisance_scores_t_xwz
Get the scores for z_xw model on the out-of-sample training data
- property nuisance_scores_tz_xw
Get the scores for tz_xw model on the out-of-sample training data
- property nuisance_scores_y_xw
Get the scores for y_xw model on the out-of-sample training data
- property nuisance_scores_z_xw
Get the scores for z_xw model on the out-of-sample training data
- property residuals_
A tuple (prel_theta, Y_res, T_res, Z_res, cov, X, W, Z), of the residuals from the first stage estimation along with the associated X, W and Z. Samples are not guaranteed to be in the same order as the input order.