econml.dynamic.dml.DynamicDML
- class econml.dynamic.dml.DynamicDML(*, model_y='auto', model_t='auto', featurizer=None, fit_cate_intercept=True, linear_first_stages=False, discrete_treatment=False, categories='auto', cv=2, mc_iters=None, mc_agg='mean', random_state=None)[source]
Bases:
econml._cate_estimator.LinearModelFinalCateEstimatorMixin
,econml._ortho_learner._OrthoLearner
CATE estimator for dynamic treatment effect estimation.
This estimator is an extension of the Double ML approach for treatments assigned sequentially over time periods.
The estimator is a special case of an
_OrthoLearner
estimator, so it follows the two stage process, where a set of nuisance functions are estimated in the first stage in a crossfitting manner and a final stage estimates the CATE model. See the documentation of_OrthoLearner
for a description of this two stage process.- Parameters
model_y (estimator or ‘auto’, optional (default is ‘auto’)) – The estimator for fitting the response to the features. Must implement fit and predict methods. If ‘auto’
WeightedLassoCV
/WeightedMultiTaskLassoCV
will be chosen.model_t (estimator or ‘auto’, optional (default is ‘auto’)) – The estimator for fitting the treatment to the features. If estimator, it must implement fit and predict methods; If ‘auto’,
LogisticRegressionCV
will be applied for discrete treatment, andWeightedLassoCV
/WeightedMultiTaskLassoCV
will be applied for continuous treatment.featurizer (transformer, optional, default None) – 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, optional, default True) – Whether the linear CATE model should have a constant term.
linear_first_stages (bool) – Whether the first stage models are linear (in which case we will expand the features passed to model_y accordingly)
discrete_treatment (bool, optional (default is
False
)) – Whether the treatment values should be treated as categorical, rather than continuous, quantitiescategories (‘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, optional (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. Iterables should make sure a group belongs to a single split.
For integer/None inputs,
GroupKFold
is usedUnless an iterable is used, we call split(X, T, groups) to generate the splits.
mc_iters (int, optional (default=None)) – The number of times to rerun the first stage models to reduce the variance of the nuisances.
mc_agg ({‘mean’, ‘median’}, optional (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, optional (default=None)) – If int, random_state is the seed used by the random number generator; IfRandomState
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 default models:
from econml.dynamic.dml import DynamicDML np.random.seed(123) n_panels = 100 # number of panels n_periods = 3 # number of time periods per panel n = n_panels * n_periods groups = np.repeat(a=np.arange(n_panels), repeats=n_periods, axis=0) X = np.random.normal(size=(n, 1)) T = np.random.normal(size=(n, 2)) y = np.random.normal(size=(n, )) est = DynamicDML() est.fit(y, T, X=X, W=None, groups=groups, inference="auto")
>>> est.const_marginal_effect(X[:2]) array([[-0.336..., -0.048..., -0.061..., 0.042..., -0.204..., 0.00667271], [-0.101..., 0.433..., 0.054..., -0.217..., -0.101..., -0.159...]]) >>> est.effect(X[:2], T0=0, T1=1) array([-0.601..., -0.091...]) >>> est.effect(X[:2], T0=np.zeros((2, n_periods*T.shape[1])), T1=np.ones((2, n_periods*T.shape[1]))) array([-0.601..., -0.091...]) >>> est.coef_ array([[ 0.112...], [ 0.231...], [ 0.055...], [-0.125...], [ 0.049...], [-0.079...]]) >>> est.coef__interval() (array([[-0.063...], [-0.009...], [-0.114...], [-0.413...], [-0.117...], [-0.262...]]), array([[0.289...], [0.471...], [0.225...], [0.163...], [0.216...], [0.103...]]))
- __init__(*, model_y='auto', model_t='auto', featurizer=None, fit_cate_intercept=True, linear_first_stages=False, discrete_treatment=False, categories='auto', cv=2, mc_iters=None, mc_agg='mean', random_state=None)[source]
Methods
__init__
(*[, model_y, model_t, featurizer, ...])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 for each time period.
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, *[, 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[, 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_
fit_cate_intercept_
The intercept in the linear model of the constant marginal treatment effect.
model_final
model_final_
models_nuisance_
models_t
models_y
nuisance_scores_t
nuisance_scores_y
original_featurizer
ortho_learner_model_final_
A tuple (y_res, T_res, X, W), of the residuals from the first stage estimation along with the associated X and W.
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 (optional (m, d_x) matrix) – 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 (optional (m, d_x) matrix) – Features for each sample
T0 (optional (m, d_t) matrix or vector of length m (Default=0)) – Base treatments for each sample
T1 (optional (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 (optional (m, d_x) matrix) – Features for each sample
T0 (optional (m, d_t) matrix or vector of length m (Default=0)) – Base treatments for each sample
T1 (optional (m, d_t) matrix or vector of length m (Default=1)) – Target treatments for each sample
alpha (optional 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)[source]
Get the output feature names.
- Parameters
feature_names (list of strings 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 constant marginal CATE model is linear. It is the names of the features that are associated with each entry of the
coef_()
parameter. Not available when the featurizer is not None and does not have a method: get_feature_names(feature_names). Otherwise None is returned.- Return type
list of strings 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 strings 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 strings
- cate_treatment_names(treatment_names=None)[source]
Get treatment names for each time period.
If the treatment is discrete, it will return expanded treatment names.
- Parameters
treatment_names (list of strings of length T.shape[1] or None) – 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 strings
- 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 (optional 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 (optional (m, d_x) matrix or None (Default=None)) – 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 notNone
, when calling the fit method.- Parameters
X (optional (m, d_x) matrix or None (Default=None)) – 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 (optional (m, d_x) matrix or None (Default=None)) – Features for each sample
alpha (optional 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 (optional (m, d_x) matrix or None (Default=None)) – 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 notNone
, when calling the fit method.- Parameters
X (optional (m, d_x) matrix or None (Default=None)) – 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 (optional (m, d_x) matrix or None (Default=None)) – Features for each sample
alpha (optional 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 (optional (m, d_x) matrix) – 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 (optional (m, d_x) matrix) – Features for each sample
T0 (optional (m, d_t) matrix or vector of length m (Default=0)) – Base treatments for each sample
T1 (optional (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 (optional (m, d_x) matrix) – Features for each sample
T0 (optional (m, d_t) matrix or vector of length m (Default=0)) – Base treatments for each sample
T1 (optional (m, d_t) matrix or vector of length m (Default=1)) – Target treatments for each sample
alpha (optional 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, *, X=None, W=None, sample_weight=None, sample_var=None, groups, cache_values=False, inference='auto')[source]
Estimate the counterfactual model from data, i.e. estimates function \(\theta(\cdot)\).
The input data must contain groups with the same size corresponding to the number of time periods the treatments were assigned over.
The data should be preferably in panel format, with groups clustered together. If group members do not appear together, the following is assumed:
the first instance of a group in the dataset is assumed to correspond to the first period of that group
the second instance of a group in the dataset is assumed to correspond to the second period of that group
…etc.
Only the value of the features X at the first period of each unit are used for heterogeneity. The value of X in subseuqnet periods is used as a time-varying control but not for heterogeneity.
- Parameters
Y ((n, d_y) matrix or vector of length n) – Outcomes for each sample (required: n = n_groups * n_periods)
T ((n, d_t) matrix or vector of length n) – Treatments for each sample (required: n = n_groups * n_periods)
X (optional(n, d_x) matrix or None (Default=None)) – Features for each sample (Required: n = n_groups * n_periods). Only first period features from each unit are used for heterogeneity, the rest are used as time-varying controls together with W
W (optional(n, d_w) matrix or None (Default=None)) – Controls for each sample (Required: n = n_groups * n_periods)
sample_weight (optional(n,) vector or None (Default=None)) – Weights for each samples
sample_var (optional(n,) vector or None (Default=None)) – Sample variance for each sample
groups ((n,) vector, required) – 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 (string,:class:.Inference instance, or None) – Method for performing inference. This estimator supports ‘bootstrap’ (or an instance of
BootstrapInference
) and ‘auto’ (or an instance ofLinearModelFinalInference
).
- Returns
self
- Return type
DynamicDML instance
- 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 (optional 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 (optional (m, d_x) matrix) – 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 (optional (m, d_x) matrix or None (Default=None)) – 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 (optional (m, d_x) matrix or None (Default=None)) – Features for each sample
alpha (optional 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 \(\{T_i, X_i\}\). Since this class assumes a linear model, the base treatment is ignored in this calculation.
- Parameters
T ((m, d_t) matrix) – Base treatments for each sample
X (optional (m, d_x) matrix) – 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 (optional (m, d_x) matrix or None (Default=None)) – 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 (optional (m, d_x) matrix or None (Default=None)) – Features for each sample
alpha (optional 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=None)
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, X=None, W=None, sample_weight=None, *, groups)[source]
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 (required: n = n_groups * n_periods)
T ((n, d_t) matrix or vector of length n) – Treatments for each sample (required: n = n_groups * n_periods)
X (optional(n, d_x) matrix or None (Default=None)) – Features for each sample (Required: n = n_groups * n_periods)
W (optional(n, d_w) matrix or None (Default=None)) – Controls for each sample (Required: n = n_groups * n_periods)
groups ((n,) vector, required) – All rows corresponding to the same group will be kept together during splitting.
- 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 (optional None or list of strings of length X.shape[1] (Default=None)) – The names of input features.
treatment_names (optional None or list (Default=None)) – The name of 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 (optional None or list (Default=None)) – The name of the outcome.
background_samples (int or None, (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 (optional 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 (optinal float (default=0)) – The mean value of the metric you’d like to test under null hypothesis.
decimals (optinal int (default=3)) – Number of decimal places to round each column to.
feature_names (optional list of strings or None (default is None)) – The input of the feature names
treatment_names (optional list of strings or None (default is None)) – The names of the treatments
output_names (optional list of strings or None (default is None)) – 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 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 residuals_
A tuple (y_res, T_res, X, W), of the residuals from the first stage estimation along with the associated X and W. Samples are not guaranteed to be in the same order as the input order.