mini_causal.partial_counter.PartialCounterfactualClassifier

class mini_causal.partial_counter.PartialCounterfactualClassifier(model, X, y)

PartialCounterfactualClassifier is class that uses partial modeling or methods to measure the impact of features on the built-in classification models.

It inherits all the methods and properties from the PartialCounterfactual.

Parameters:

• model – the trained model

• X (pd.DataFrame) – The features stored in a dataframe format dataframe are used to simplify accessing the features for partial modeling and calculating causal metrics

• {pd.DataFrame (y) – The target values must have the same length as the X arg

• np.array} – The target values must have the same length as the X arg

partial_fit

fits the partial estimator i.e the model without the feature

individual_causal_effects

returns an array of the individual causal effects

Type:

np.ndarray

average_causal_effects

returns the average causal effect float value

Type:

np.ndarray

References

[1]

GUIDO W. IMBENS and DONALD B. RUBIN, Causal Inference For Statistics,Social and Biomedical Sciences An Introduction

[2]

Victor Chernozhukov et al. ,Applied Causal Inference Powered by ML and AI https://causalml-book.org/

__init__(model, X, y)

Methods

__init__(model, X, y)
average_causal_effect(X, feature)	The average causal effect
average_causal_effect_probs(X, feature)	The average causal effect on the predicted probabilities
individual_causal_effects(X, feature)	The individual causal effects of the predicted
individual_causal_effects_probs(X, feature)	The individual causal effects of the predicted probabilities
partial_model(feature)	Clone the parameters from the fitted model Then build a partial model from the scratch