CEInterpreter

For the CATE \(\tau(v)\) estimated by an estimator model, e.g., double machine learning model, CEInterpreter interprets the results by building a decision tree to model the relationships between \(\tau(v)\) and the covariates \(v\). Then one can use the decision rules of the fitted tree model to analyze \(\tau(v)\).

Class Structures

class ylearn.effect_interpreter.ce_interpreter.CEInterpreter(*, criterion='squared_error', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, random_state=2022, max_leaf_nodes=None, max_features=None, min_impurity_decrease=0.0, min_weight_fraction_leaf=0.0, ccp_alpha=0.0, categories='auto')

Parameters

• criterion ({"squared_error", "friedman_mse", "absolute_error", "poisson"}, default="squared_error") – The function to measure the quality of a split. Supported criteria are “squared_error” for the mean squared error, which is equal to variance reduction as feature selection criterion and minimizes the L2 loss using the mean of each terminal node, “friedman_mse”, which uses mean squared error with Friedman’s improvement score for potential splits, “absolute_error” for the mean absolute error, which minimizes the L1 loss using the median of each terminal node, and “poisson” which uses reduction in Poisson deviance to find splits.

• splitter ({"best", "random"}, default="best") – The strategy used to choose the split at each node. Supported strategies are “best” to choose the best split and “random” to choose the best random split.

• max_depth (int, default=None) – 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 or float, default=2) – The minimum number of 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 or 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 training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.

  • 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=0.0) – The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.

• max_features (int, float or {"sqrt", "log2"}, default=None) –

  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 “sqrt”, then max_features=sqrt(n_features).

  • If “log2”, then max_features=log2(n_features).

  • If None, then max_features=n_features.

• random_state (int) – Controls the randomness of the estimator.

• max_leaf_nodes (int, default to None) – Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.

• min_impurity_decrease (float, default=0.0) –

  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 samples, N_t is the number of samples at the current node, N_t_L is the number of samples in the left child, and N_t_R is the number of 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.

fit(data, est_model, **kwargs)

Fit the CEInterpreter model to interpret the causal effect estimated by the est_model on data.

Parameters

• data (pandas.DataFrame) – The input samples for the est_model to estimate the causal effects and for the CEInterpreter to fit.

• est_model (estimator_model) – est_model should be any valid estimator model of ylearn which was already fitted and can estimate the CATE.

Returns

Fitted CEInterpreter

Return type

instance of CEInterpreter

interpret(*, v=None, data=None)

Interpret the fitted model in the test data.

Parameters

• v (numpy.ndarray, optional, default=None) – The test covariates in the form of ndarray. If this is given, then data will be ignored and the model will use this as the test data.

• data (pandas.DataFrame, optional, default=None) – The test data in the form of the DataFrame. The model will only use this if v is set as None. In this case, if data is also None, then the data used for training will be used.

Returns

The interpreted results for all examples.

Return type

dict

plot(*, feature_names=None, max_depth=None, class_names=None, label='all', filled=False, node_ids=False, proportion=False, rounded=False, precision=3, ax=None, fontsize=None)

Plot the fitted tree model. The sample counts that are shown are weighted with any sample_weights that might be present. The visualization is fit automatically to the size of the axis. Use the figsize or dpi arguments of plt.figure to control the size of the rendering.

Returns

List containing the artists for the annotation boxes making up the tree.

Return type

annotations : list of artists

Example

pass