econml.dr.ForestDRLearner

class econml.dr.ForestDRLearner(*, model_regression='auto', model_propensity='auto', discrete_outcome=False, featurizer=None, min_propensity=1e-06, categories='auto', cv=2, mc_iters=None, mc_agg='mean', n_estimators=1000, max_depth=None, min_samples_split=5, min_samples_leaf=5, min_weight_fraction_leaf=0.0, max_features='auto', min_impurity_decrease=0.0, max_samples=0.45, min_balancedness_tol=0.45, honest=True, subforest_size=4, n_jobs=- 1, verbose=0, random_state=None, allow_missing=False, use_ray=False, ray_remote_func_options=None)[source]

Bases: econml._cate_estimator.ForestModelFinalCateEstimatorDiscreteMixin, econml.dr._drlearner.DRLearner

Instance of DRLearner with a RegressionForest as a final model, so as to enable non-parametric inference.

Parameters

model_propensity (estimator, default 'auto') – Classifier for Pr[T=t | X, W]. Trained by regressing treatments on (features, controls) concatenated.
- If 'auto', the model will be the best-fitting of a set of linear and forest models
- Otherwise, see Model Selection for the range of supported options
model_regression (estimator, default 'auto') – Estimator for E[Y | X, W, T]. Trained by regressing Y on (features, controls, one-hot-encoded treatments) concatenated. The one-hot-encoding excludes the baseline treatment.
- If 'auto', the model will be the best-fitting of a set of linear and forest models
- Otherwise, see Model Selection for the range of supported options; if a single model is specified it should be a classifier if discrete_outcome is True and a regressor otherwise
discrete_outcome (bool, default False) – Whether the outcome should be treated as binary
min_propensity (float, default 1e-6) – The minimum propensity at which to clip propensity estimates to avoid dividing by zero.
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.
- CV splitter
- 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.
n_estimators (int, default 100) – The total number of trees in the forest. The forest consists of a forest of sqrt(n_estimators) sub-forests, where each sub-forest contains sqrt(n_estimators) trees.
max_depth (int or None, optional) – The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.
min_samples_split (int, float, default 2) – The minimum number of splitting samples required to split an internal node.
- If int, then consider min_samples_split as the minimum number.
- If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.
min_samples_leaf (int, float, default 1) – The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min_samples_leaf splitting samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. After construction the tree is also pruned so that there are at least min_samples_leaf estimation samples on each leaf.
- If int, then consider min_samples_leaf as the minimum number.
- If float, then min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.
min_weight_fraction_leaf (float, default 0.) – The minimum weighted fraction of the sum total of weights (of all splitting samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. After construction the tree is pruned so that the fraction of the sum total weight of the estimation samples contained in each leaf node is at least min_weight_fraction_leaf
max_features (int, float, str, or None, default “auto”) – The number of features to consider when looking for the best split:
- If int, then consider max_features features at each split.
- If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split.
- If “auto”, then max_features=n_features.
- If “sqrt”, then max_features=sqrt(n_features).
- If “log2”, then max_features=log2(n_features).
- If None, then max_features=n_features.
Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than max_features features.
min_impurity_decrease (float, default 0.) – A node will be split if this split induces a decrease of the impurity greater than or equal to this value.

The weighted impurity decrease equation is the following:
```
N_t / N * (impurity - N_t_R / N_t * right_impurity
                    - N_t_L / N_t * left_impurity)
```
where N is the total number of split samples, N_t is the number of split samples at the current node, N_t_L is the number of split samples in the left child, and N_t_R is the number of split samples in the right child.

N, N_t, N_t_R and N_t_L all refer to the weighted sum, if sample_weight is passed.
max_samples (int or float in (0, .5], default .45,) – The number of samples to use for each subsample that is used to train each tree:
- If int, then train each tree on max_samples samples, sampled without replacement from all the samples
- If float, then train each tree on ceil(max_samples * n_samples), sampled without replacement from all the samples.
min_balancedness_tol (float in [0, .5], default .45) – How imbalanced a split we can tolerate. This enforces that each split leaves at least (.5 - min_balancedness_tol) fraction of samples on each side of the split; or fraction of the total weight of samples, when sample_weight is not None. Default value, ensures that at least 5% of the parent node weight falls in each side of the split. Set it to 0.0 for no balancedness and to .5 for perfectly balanced splits. For the formal inference theory to be valid, this has to be any positive constant bounded away from zero.
honest (bool, default True) – Whether to use honest trees, i.e. half of the samples are used for creating the tree structure and the other half for the estimation at the leafs. If False, then all samples are used for both parts.
subforest_size (int, default 4,) – The number of trees in each sub-forest that is used in the bootstrap-of-little-bags calculation. The parameter n_estimators must be divisible by subforest_size. Should typically be a small constant.
n_jobs (int or None, default -1) – The number of jobs to run in parallel for both fit and predict. None means 1 unless in a joblib.parallel_backend() context. -1 means using all processors. See Glossary for more details.
verbose (int, default 0) – Controls the verbosity when fitting and predicting.
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 the RandomState instance used by np.random.
allow_missing (bool) – Whether to allow missing values in W. If True, will need to supply model_propensity and model_regression that can handle missing values.
use_ray (bool, default False) – Whether to use Ray to parallelize the cross-validation step. If True, Ray must be installed.
ray_remote_func_options (dict, default None) – Options to pass to the remote function when using Ray. See https://docs.ray.io/en/latest/ray-core/api/doc/ray.remote.html

__init__(*, model_regression='auto', model_propensity='auto', discrete_outcome=False, featurizer=None, min_propensity=1e-06, categories='auto', cv=2, mc_iters=None, mc_agg='mean', n_estimators=1000, max_depth=None, min_samples_split=5, min_samples_leaf=5, min_weight_fraction_leaf=0.0, max_features='auto', min_impurity_decrease=0.0, max_samples=0.45, min_balancedness_tol=0.45, honest=True, subforest_size=4, n_jobs=- 1, verbose=0, random_state=None, allow_missing=False, use_ray=False, ray_remote_func_options=None)[source]

Methods

`__init__`(*[, model_regression, ...])
`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.
`const_marginal_ate`([X])	Calculate the average constant marginal CATE \(E_X[\theta(X)]\).
`const_marginal_ate_inference`([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.
`const_marginal_effect`([X])	Calculate the constant marginal CATE \(\theta(·)\).
`const_marginal_effect_inference`([X])	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.
`feature_importances_`(T)
`fit`(Y, T, *[, X, W, sample_weight, groups, ...])	Estimate the counterfactual model from data, i.e. estimates functions τ(·,·,·), ∂τ(·,·).
`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.
`model_cate`([T])	Get the fitted final CATE model.
`multitask_model_cate`()	Get the fitted final CATE model.
`refit_final`(*[, inference])	Estimate the counterfactual model using a new final model specification but with cached first stage results.
`score`(Y, T[, 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)

Attributes

`dowhy`	Get an instance of `DoWhyWrapper` to allow other functionalities from dowhy package.
`featurizer_`	Get the fitted featurizer.
`fitted_models_final`
`model_final`
`model_final_`
`models_nuisance_`
`models_propensity`	Get the fitted propensity models.
`models_regression`	Get the fitted regression models.
`multitask_model_final`
`nuisance_scores_propensity`	Gets the score for the propensity model on out-of-sample training data
`nuisance_scores_regression`	Gets the score for the regression model on out-of-sample training data
`ortho_learner_model_final_`
`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 not None, 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

object

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 not None, 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 of ate(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

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 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) matrix

const_marginal_ate_inference(X=None)

Inference results for the quantities \(E_X[\theta(X)]\) produced by the model. Available only when inference is not None, 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: object

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 not None, 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 of const_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 treatment on each outcome for each sample X[i]. 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) matrix or (d_y, d_t) matrix if X is None

const_marginal_effect_inference(X=None)

Inference results for the quantities \(\theta(X)\) produced by the model. Available only when inference is not None, 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: object

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 not None, 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 of const_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 not None, 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

object

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 not None, 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 of effect(X, T0, T1)) )

fit(Y, T, *, X=None, W=None, sample_weight=None, groups=None, cache_values=False, inference='auto')[source]

Estimate the counterfactual model from data, i.e. estimates functions τ(·,·,·), ∂τ(·,·).

Parameters

Y ((n × d_y) matrix or vector of length n) – Outcomes for each sample
T ((n × dₜ) matrix or vector of length n) – Treatments for each sample
X ((n × dₓ) matrix, optional) – Features for each sample
W ((n × d_w) matrix, optional) – Controls for each sample
sample_weight ((n,) array_like or None) – 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 of BootstrapInference) and ‘blb’ (for Bootstrap-of-Little-Bags based inference)

Return type

self

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 not None, 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

object

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 not None, 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 of marginal_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 not None, 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

object

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 not None, 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 of marginal_effect(T, X) )

model_cate(T=1)

Get the fitted final CATE model.

Parameters: T (alphanumeric) – The treatment with respect to which we want the fitted CATE model.
Returns: model_cate – An instance of the model_final object that was fitted after calling fit which corresponds to the CATE model for treatment T=t, compared to baseline. Available when multitask_model_final=False.
Return type: object of type(model_final)

multitask_model_cate()[source]

Get the fitted final CATE model.

Returns: multitask_model_cate – An instance of the model_final object that was fitted after calling fit which corresponds whose vector of outcomes correspond to the CATE model for each treatment, compared to baseline. Available only when multitask_model_final=True.
Return type: object of type(model_final)

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 with cache_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: object

score(Y, T, 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,) vector of length n) – Outcomes for each sample
T ((n,) vector of length n) – Treatments 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

float

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

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 models_propensity

Get the fitted propensity models.

Returns: models_propensity – A nested list of instances of the model_propensity 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_propensity)

property models_regression

Get the fitted regression models.

Returns: model_regression – A nested list of instances of the model_regression 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_regression)

property nuisance_scores_propensity: Gets the score for the propensity model on out-of-sample training data

property nuisance_scores_regression: Gets the score for the regression model on out-of-sample training data