2.10. doubleml.DoubleMLPQ#

class doubleml.DoubleMLPQ(obj_dml_data, ml_g, ml_m, treatment=1, quantile=0.5, n_folds=5, n_rep=1, score='PQ', normalize_ipw=True, kde=None, trimming_rule='truncate', trimming_threshold=0.01, draw_sample_splitting=True)#

Double machine learning for potential quantiles

Parameters:

obj_dml_data (DoubleMLData object) – The DoubleMLData object providing the data and specifying the variables for the causal model.
ml_g (classifier implementing fit() and predict()) – A machine learner implementing fit() and predict_proba() methods (e.g. sklearn.ensemble.RandomForestClassifier) for the nuisance function \(g_0(X) = E[Y <= \theta | X, D=d]\) .
ml_m (classifier implementing fit() and predict_proba()) – A machine learner implementing fit() and predict_proba() methods (e.g. sklearn.ensemble.RandomForestClassifier) for the nuisance function \(m_0(X) = E[D=d|X]\).
treatment (int) – Binary treatment indicator. Has to be either 0 or 1. Determines the potential outcome to be considered. Default is 1.
quantile (float) – Quantile of the potential outcome. Has to be between 0 and 1. Default is 0.5.
n_folds (int) – Number of folds. Default is 5.
n_rep (int) – Number of repetitons for the sample splitting. Default is 1.
score (str) – A str ('PQ' is the only choice) specifying the score function for potential quantiles. Default is 'PQ'.
normalize_ipw (bool) – Indicates whether the inverse probability weights are normalized. Default is True.
kde (callable or None) – A callable object / function with signature deriv = kde(u, weights) for weighted kernel density estimation. Here deriv should evaluate the density in 0. Default is 'None', which uses statsmodels.nonparametric.kde.KDEUnivariate with a gaussian kernel and silverman for bandwidth determination.
trimming_rule (str) – A str ('truncate' is the only choice) specifying the trimming approach. Default is 'truncate'.
trimming_threshold (float) – The threshold used for trimming. Default is 1e-2.
draw_sample_splitting (bool) – Indicates whether the sample splitting should be drawn during initialization of the object. Default is True.

Examples

>>> import numpy as np
>>> import doubleml as dml
>>> from doubleml.datasets import make_irm_data
>>> from sklearn.ensemble import RandomForestClassifier
>>> np.random.seed(3141)
>>> ml_g = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=10, min_samples_leaf=2)
>>> ml_m = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=10, min_samples_leaf=2)
>>> data = make_irm_data(theta=0.5, n_obs=500, dim_x=20, return_type='DataFrame')
>>> obj_dml_data = dml.DoubleMLData(data, 'y', 'd')
>>> dml_pq_obj = dml.DoubleMLPQ(obj_dml_data, ml_g, ml_m, treatment=1, quantile=0.5)
>>> dml_pq_obj.fit().summary
       coef   std err         t     P>|t|     2.5 %    97.5 %
d  0.553878  0.149858  3.696011  0.000219  0.260161  0.847595

Methods

`bootstrap`([method, n_rep_boot])	Multiplier bootstrap for DoubleML models.
`confint`([joint, level])	Confidence intervals for DoubleML models.
`construct_framework`()	Construct a `doubleml.DoubleMLFramework` object.
`draw_sample_splitting`()	Draw sample splitting for DoubleML models.
`evaluate_learners`([learners, metric])	Evaluate fitted learners for DoubleML models on cross-validated predictions.
`fit`([n_jobs_cv, store_predictions, ...])	Estimate DoubleML models.
`get_params`(learner)	Get hyperparameters for the nuisance model of DoubleML models.
`p_adjust`([method])	Multiple testing adjustment for DoubleML models.
`sensitivity_analysis`([cf_y, cf_d, rho, ...])	Performs a sensitivity analysis to account for unobserved confounders.
`sensitivity_benchmark`(benchmarking_set[, ...])	Computes a benchmark for a given set of features.
`sensitivity_plot`([idx_treatment, value, ...])	Contour plot of the sensivity with respect to latent/confounding variables.
`set_ml_nuisance_params`(learner, treat_var, ...)	Set hyperparameters for the nuisance models of DoubleML models.
`set_sample_splitting`(all_smpls[, ...])	Set the sample splitting for DoubleML models.
`tune`(param_grids[, tune_on_folds, ...])	Hyperparameter-tuning for DoubleML models.

Attributes

`all_coef`	Estimates of the causal parameter(s) for the `n_rep` different sample splits after calling `fit()`.
`all_se`	Standard errors of the causal parameter(s) for the `n_rep` different sample splits after calling `fit()`.
`boot_method`	The method to construct the bootstrap replications.
`boot_t_stat`	Bootstrapped t-statistics for the causal parameter(s) after calling `fit()` and `bootstrap()`.
`coef`	Estimates for the causal parameter(s) after calling `fit()`.
`framework`	The corresponding `doubleml.DoubleMLFramework` object.
`kde`	The kernel density estimation of the derivative.
`learner`	The machine learners for the nuisance functions.
`learner_names`	The names of the learners.
`models`	The fitted nuisance models.
`n_folds`	Number of folds.
`n_rep`	Number of repetitions for the sample splitting.
`n_rep_boot`	The number of bootstrap replications.
`normalize_ipw`	Indicates whether the inverse probability weights are normalized.
`nuisance_loss`	The losses of the nuisance models (root-mean-squared-errors or logloss).
`nuisance_targets`	The outcome of the nuisance models.
`params`	The hyperparameters of the learners.
`params_names`	The names of the nuisance models with hyperparameters.
`predictions`	The predictions of the nuisance models in form of a dictinary.
`psi`	Values of the score function after calling `fit()`; For models (e.g., PLR, IRM, PLIV, IIVM) with linear score (in the parameter) \(\psi(W; \theta, \eta) = \psi_a(W; \eta) \theta + \psi_b(W; \eta)\).
`psi_deriv`	Values of the derivative of the score function with respect to the parameter \(\theta\) after calling `fit()`; For models (e.g., PLR, IRM, PLIV, IIVM) with linear score (in the parameter) \(\psi_a(W; \eta)\).
`psi_elements`	Values of the score function components after calling `fit()`; For models (e.g., PLR, IRM, PLIV, IIVM) with linear score (in the parameter) a dictionary with entries `psi_a` and `psi_b` for \(\psi_a(W; \eta)\) and \(\psi_b(W; \eta)\).
`pval`	p-values for the causal parameter(s) after calling `fit()`.
`quantile`	Quantile for potential outcome.
`score`	The score function.
`se`	Standard errors for the causal parameter(s) after calling `fit()`.
`sensitivity_elements`	Values of the sensitivity components after calling `fit()`; If available (e.g., PLR, IRM) a dictionary with entries `sigma2`, `nu2`, `psi_sigma2`, `psi_nu2` and `riesz_rep`.
`sensitivity_params`	Values of the sensitivity parameters after calling `sesitivity_analysis()`; If available (e.g., PLR, IRM) a dictionary with entries `theta`, `se`, `ci`, `rv` and `rva`.
`sensitivity_summary`	Returns a summary for the sensitivity analysis after calling `sensitivity_analysis()`.
`smpls`	The partition used for cross-fitting.
`smpls_cluster`	The partition of clusters used for cross-fitting.
`summary`	A summary for the estimated causal effect after calling `fit()`.
`t_stat`	t-statistics for the causal parameter(s) after calling `fit()`.
`treatment`	Treatment indicator for potential outcome.
`trimming_rule`	Specifies the used trimming rule.
`trimming_threshold`	Specifies the used trimming threshold.

DoubleMLPQ.bootstrap(method='normal', n_rep_boot=500)#

Multiplier bootstrap for DoubleML models.

Parameters:

method (str) – A str ('Bayes', 'normal' or 'wild') specifying the multiplier bootstrap method. Default is 'normal'
n_rep_boot (int) – The number of bootstrap replications.

Returns:

self

Return type:

object

DoubleMLPQ.confint(joint=False, level=0.95)#

Confidence intervals for DoubleML models.

Parameters:

joint (bool) – Indicates whether joint confidence intervals are computed. Default is False
level (float) – The confidence level. Default is 0.95.

Returns:

df_ci – A data frame with the confidence interval(s).

Return type:

pd.DataFrame

DoubleMLPQ.construct_framework()#

Construct a doubleml.DoubleMLFramework object. Can be used to construct e.g. confidence intervals.

Returns:: doubleml_framework
Return type:: doubleml.DoubleMLFramework

DoubleMLPQ.draw_sample_splitting()#

Draw sample splitting for DoubleML models.

The samples are drawn according to the attributes n_folds and n_rep.

Returns:: self
Return type:: object

DoubleMLPQ.evaluate_learners(learners=None, metric=<function _rmse>)#

Evaluate fitted learners for DoubleML models on cross-validated predictions.

Parameters:

learners (list) – A list of strings which correspond to the nuisance functions of the model.
metric (callable) – A callable function with inputs y_pred and y_true of shape (1, n), where n specifies the number of observations. Remark that some models like IRM are not able to provide all values for y_true for all learners and might contain some nan values in the target vector. Default is the root-mean-square error.

Returns:

dist – A dictionary containing the evaluated metric for each learner.

Return type:

dict

Examples

>>> import numpy as np
>>> import doubleml as dml
>>> from sklearn.metrics import mean_absolute_error
>>> from doubleml.datasets import make_irm_data
>>> from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
>>> np.random.seed(3141)
>>> ml_g = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)
>>> ml_m = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)
>>> data = make_irm_data(theta=0.5, n_obs=500, dim_x=20, return_type='DataFrame')
>>> obj_dml_data = dml.DoubleMLData(data, 'y', 'd')
>>> dml_irm_obj = dml.DoubleMLIRM(obj_dml_data, ml_g, ml_m)
>>> dml_irm_obj.fit()
>>> def mae(y_true, y_pred):
>>>     subset = np.logical_not(np.isnan(y_true))
>>>     return mean_absolute_error(y_true[subset], y_pred[subset])
>>> dml_irm_obj.evaluate_learners(metric=mae)
{'ml_g0': array([[0.85974356]]),
 'ml_g1': array([[0.85280376]]),
 'ml_m': array([[0.35365143]])}

DoubleMLPQ.fit(n_jobs_cv=None, store_predictions=True, external_predictions=None, store_models=False)#

Estimate DoubleML models.

Parameters:

n_jobs_cv (None or int) – The number of CPUs to use to fit the learners. None means 1. Default is None.
store_predictions (bool) – Indicates whether the predictions for the nuisance functions should be stored in predictions. Default is True.
store_models (bool) – Indicates whether the fitted models for the nuisance functions should be stored in models. This allows to analyze the fitted models or extract information like variable importance. Default is False.
external_predictions (None or dict) – If None all models for the learners are fitted and evaluated. If a dictionary containing predictions for a specific learner is supplied, the model will use the supplied nuisance predictions instead. Has to be a nested dictionary where the keys refer to the treatment and the keys of the nested dictionarys refer to the corresponding learners. Default is None.

Returns:

self

Return type:

object

DoubleMLPQ.get_params(learner)#

Get hyperparameters for the nuisance model of DoubleML models.

Parameters:: learner (str) – The nuisance model / learner (see attribute params_names).
Returns:: params – Parameters for the nuisance model / learner.
Return type:: dict

DoubleMLPQ.p_adjust(method='romano-wolf')#

Multiple testing adjustment for DoubleML models.

Parameters:: method (str) – A str ('romano-wolf'', 'bonferroni', 'holm', etc) specifying the adjustment method. In addition to 'romano-wolf'', all methods implemented in statsmodels.stats.multitest.multipletests() can be applied. Default is 'romano-wolf'.
Returns:: p_val – A data frame with adjusted p-values.
Return type:: pd.DataFrame

DoubleMLPQ.sensitivity_analysis(cf_y=0.03, cf_d=0.03, rho=1.0, level=0.95, null_hypothesis=0.0)#

Performs a sensitivity analysis to account for unobserved confounders.

The evaluated scenario is stored as a dictionary in the property sensitivity_params.

Parameters:

cf_y (float) – Percentage of the residual variation of the outcome explained by latent/confounding variables. Default is 0.03.
cf_d (float) – Percentage gains in the variation of the Riesz representer generated by latent/confounding variables. Default is 0.03.
rho (float) – The correlation between the differences in short and long representations in the main regression and Riesz representer. Has to be in [-1,1]. The absolute value determines the adversarial strength of the confounding (maximizes at 1.0). Default is 1.0.
level (float) – The confidence level. Default is 0.95.
null_hypothesis (float or numpy.ndarray) – Null hypothesis for the effect. Determines the robustness values. If it is a single float uses the same null hypothesis for all estimated parameters. Else the array has to be of shape (n_coefs,). Default is 0.0.

Returns:

self

Return type:

object

DoubleMLPQ.sensitivity_benchmark(benchmarking_set, fit_args=None)#: Computes a benchmark for a given set of features. Returns a DataFrame containing the corresponding values for cf_y, cf_d, rho and the change in estimates. :returns: benchmark_results – Benchmark results. :rtype: pandas.DataFrame

DoubleMLPQ.sensitivity_plot(idx_treatment=0, value='theta', rho=1.0, level=0.95, null_hypothesis=0.0, include_scenario=True, benchmarks=None, fill=True, grid_bounds=(0.15, 0.15), grid_size=100)#

Contour plot of the sensivity with respect to latent/confounding variables.

Parameters:

idx_treatment (int) – Index of the treatment to perform the sensitivity analysis. Default is 0.
value (str) – Determines which contours to plot. Valid values are 'theta' (refers to the bounds) and 'ci' (refers to the bounds including statistical uncertainty). Default is 'theta'.
rho (float) – The correlation between the differences in short and long representations in the main regression and Riesz representer. Has to be in [-1,1]. The absolute value determines the adversarial strength of the confounding (maximizes at 1.0). Default is 1.0.
level (float) – The confidence level. Default is 0.95.
null_hypothesis (float) – Null hypothesis for the effect. Determines the direction of the contour lines.
include_scenario (bool) – Indicates whether to highlight the scenario from the call of sensitivity_analysis(). Default is True.
benchmarks (dict or None) – Dictionary of benchmarks to be included in the plot. The keys are cf_y, cf_d and name. Default is None.
fill (bool) – Indicates whether to use a heatmap style or only contour lines. Default is True.
grid_bounds (tuple) – Determines the evaluation bounds of the grid for cf_d and cf_y. Has to contain two floats in [0, 1). Default is (0.15, 0.15).
grid_size (int) – Determines the number of evaluation points of the grid. Default is 100.

Returns:

fig – Plotly figure of the sensitivity contours.

Return type:

object

DoubleMLPQ.set_ml_nuisance_params(learner, treat_var, params)#

Set hyperparameters for the nuisance models of DoubleML models.

Parameters:

learner (str) – The nuisance model / learner (see attribute params_names).
treat_var (str) – The treatment variable (hyperparameters can be set treatment-variable specific).
params (dict or list) – A dict with estimator parameters (used for all folds) or a nested list with fold specific parameters. The outer list needs to be of length n_rep and the inner list of length n_folds.

Returns:

self

Return type:

object

DoubleMLPQ.set_sample_splitting(all_smpls, all_smpls_cluster=None)#

Set the sample splitting for DoubleML models.

The attributes n_folds and n_rep are derived from the provided partition.

Parameters:

all_smpls (list or tuple) –

If nested list of lists of tuples:
The outer list needs to provide an entry per repeated sample splitting (length of list is set as n_rep). The inner list needs to provide a tuple (train_ind, test_ind) per fold (length of list is set as n_folds). test_ind must form a partition for each inner list.

If list of tuples:
The list needs to provide a tuple (train_ind, test_ind) per fold (length of list is set as n_folds). test_ind must form a partition. n_rep=1 is always set.

If tuple:
Must be a tuple with two elements train_ind and test_ind. Only viable option is to set train_ind and test_ind to np.arange(n_obs), which corresponds to no sample splitting. n_folds=1 and n_rep=1 is always set.
all_smpls_cluster (list or None) – Nested list or None. The first level of nesting corresponds to the number of repetitions. The second level of nesting corresponds to the number of folds. The third level of nesting contains a tuple of training and testing lists. Both training and testing contain an array for each cluster variable, which form a partition of the clusters. Default is None.

Returns:

self

Return type:

object

Examples

>>> import numpy as np
>>> import doubleml as dml
>>> from doubleml.datasets import make_plr_CCDDHNR2018
>>> from sklearn.ensemble import RandomForestRegressor
>>> from sklearn.base import clone
>>> np.random.seed(3141)
>>> learner = RandomForestRegressor(max_depth=2, n_estimators=10)
>>> ml_g = learner
>>> ml_m = learner
>>> obj_dml_data = make_plr_CCDDHNR2018(n_obs=10, alpha=0.5)
>>> dml_plr_obj = dml.DoubleMLPLR(obj_dml_data, ml_g, ml_m)
>>> # simple sample splitting with two folds and without cross-fitting
>>> smpls = ([0, 1, 2, 3, 4], [5, 6, 7, 8, 9])
>>> dml_plr_obj.set_sample_splitting(smpls)
>>> # sample splitting with two folds and cross-fitting
>>> smpls = [([0, 1, 2, 3, 4], [5, 6, 7, 8, 9]),
>>>          ([5, 6, 7, 8, 9], [0, 1, 2, 3, 4])]
>>> dml_plr_obj.set_sample_splitting(smpls)
>>> # sample splitting with two folds and repeated cross-fitting with n_rep = 2
>>> smpls = [[([0, 1, 2, 3, 4], [5, 6, 7, 8, 9]),
>>>           ([5, 6, 7, 8, 9], [0, 1, 2, 3, 4])],
>>>          [([0, 2, 4, 6, 8], [1, 3, 5, 7, 9]),
>>>           ([1, 3, 5, 7, 9], [0, 2, 4, 6, 8])]]
>>> dml_plr_obj.set_sample_splitting(smpls)

DoubleMLPQ.tune(param_grids, tune_on_folds=False, scoring_methods=None, n_folds_tune=5, search_mode='grid_search', n_iter_randomized_search=100, n_jobs_cv=None, set_as_params=True, return_tune_res=False)#

Hyperparameter-tuning for DoubleML models.

The hyperparameter-tuning is performed using either an exhaustive search over specified parameter values implemented in sklearn.model_selection.GridSearchCV or via a randomized search implemented in sklearn.model_selection.RandomizedSearchCV.

Parameters:

param_grids (dict) – A dict with a parameter grid for each nuisance model / learner (see attribute learner_names).
tune_on_folds (bool) – Indicates whether the tuning should be done fold-specific or globally. Default is False.
scoring_methods (None or dict) – The scoring method used to evaluate the predictions. The scoring method must be set per nuisance model via a dict (see attribute learner_names for the keys). If None, the estimator’s score method is used. Default is None.
n_folds_tune (int) – Number of folds used for tuning. Default is 5.
search_mode (str) – A str ('grid_search' or 'randomized_search') specifying whether hyperparameters are optimized via sklearn.model_selection.GridSearchCV or sklearn.model_selection.RandomizedSearchCV. Default is 'grid_search'.
n_iter_randomized_search (int) – If search_mode == 'randomized_search'. The number of parameter settings that are sampled. Default is 100.
n_jobs_cv (None or int) – The number of CPUs to use to tune the learners. None means 1. Default is None.
set_as_params (bool) – Indicates whether the hyperparameters should be set in order to be used when fit() is called. Default is True.
return_tune_res (bool) – Indicates whether detailed tuning results should be returned. Default is False.

Returns:

self (object) – Returned if return_tune_res is False.
tune_res (list) – A list containing detailed tuning results and the proposed hyperparameters. Returned if return_tune_res is True.