CIFRank

@article{yang2020causal,

title={Causal intersectionality for fair ranking}, author={Yang, Ke and Loftus, Joshua R and Stoyanovich, Julia}, journal={arXiv preprint arXiv:2006.08688}, year={2020}

}

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class fairdiverse.search.preprocessing_model.CIFRank.CIFRank(configs, dataset)

Bases: PreprocessingFairnessIntervention

CIFRank class implements a fairness intervention method using causal intersectionality.

This class extends the PreprocessingFairnessIntervention class and applies a causal fairness ranking model to the given dataset.

fit(X_train, run)

Train the causal fairness ranking model on the given training dataset.

This method ensures that the necessary model directory exists and then runs the causal model. The trained model is saved in self.model_path.

:param X_trainpandas.DataFrame

The training dataset.

:param runstr

The identifier for the training run.

:return : None

generate_counterfactual_data(X, run)

Generates the fair data by using the causal estimates :param data: data on which to append the fair columns :type data: pandas.Dataframe

Returns:

data containing the appended fair columns which contain the counterfactual values computed based on the causal estimates

Return type:

data (pandas.Dataframe)

transform(X, run, file_name=None)

Apply the fairness transformation to the dataset by generating fair counterfactual columns.

Fair columns are generated using a naming convention <column_name>_fair and saved to a CSV file if it does not already exist.

:param Xpandas.DataFrame

The dataset to which the fairness method should be applied.

:param runstr

The identifier for the transformation run.

:param file_namestr, optional

Name of the file to save the transformed dataset.

:returnpandas.DataFrame

The dataset with appended fair columns.