4.1.4. doubleml.utils.DoubleMLPolicyTree

class doubleml.utils.DoubleMLPolicyTree(orth_signal, features, depth=2, **tree_params)

Policy Tree fitting for DoubleML. Currently avaivable for IRM models.

Parameters:

• orth_signal (numpy.array) – The orthogonal signal to be predicted. Has to be of shape (n_obs,), where n_obs is the number of observations.

• features (pandas.DataFrame) – The covariates for estimating the policy tree. Has to have the shape (n_obs, d), where n_obs is the number of observations and d is the number of predictors.

• depth (int) – The depth of the policy tree that will be built. Default is 2.

• **tree_params (dict) – Parameters that are forwarded to the sklearn.tree.DecisionTreeClassifier. Note that by default we perform minimal pruning by setting the ccp_alpha = 0.01 and min_samples_leaf = 8. This can be adjusted.

Methods

fit()	Estimate DoubleMLPolicyTree models.
plot_tree()	Plots the DoubleMLPolicyTree.
predict(features)	Predicts policy based on the DoubleMLPolicyTree.

Attributes

features	Covariates.
orth_signal	Orthogonal signal.
policy_tree	Policy tree model.
summary	A summary for the policy tree.

DoubleMLPolicyTree.fit()

Estimate DoubleMLPolicyTree models.

Returns:

self

Return type:

object

DoubleMLPolicyTree.plot_tree()

Plots the DoubleMLPolicyTree.

Returns:

self

Return type:

object

DoubleMLPolicyTree.predict(features)

Predicts policy based on the DoubleMLPolicyTree.

Parameters:

features (pandas.DataFrame) – The covariates for predicting based on the policy tree. Has to have the shape (n_obs, d), where n_obs is the number of observations and d is the number of predictors. Has to have the identical keys as the original covariates.

Returns:

self

Return type:

object