causal-hub 0.0.4

A library for causal models, inference and discovery.
Documentation 
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use crate::{
    datasets::{CatIncTable, IncDataset, MissingMethod},
    estimators::{CSSEstimator, ParCSSEstimator, SSE},
    models::{CatCPDS, Labelled},
    types::Set,
};

impl CSSEstimator<CatCPDS> for SSE<'_, CatIncTable> {
    fn fit(&self, x: &Set<usize>, z: &Set<usize>) -> CatCPDS {
        // Get the union of X and Z.
        let x_z = Some(&(x | z));
        // Get the missing method or default to PW.
        let m = self.missing_method.as_ref().unwrap_or(&MissingMethod::PW);
        // Get the missing mechanism or default to None.
        let r = self.missing_mechanism.as_ref();

        // Apply the missing handling method.
        let d = self.dataset.apply_missing_method(m, x_z, r);

        // Get the labels of the original dataset.
        let labels = self.dataset.labels();
        // Map the indices from the original dataset to the new one.
        let x = &d.indices_from(x, labels);
        let z = &d.indices_from(z, labels);

        // Estimate based on the resulting dataset.
        d.map_either(
            |d| SSE::new(&d).fit(x, z), // Complete case.
            |d| SSE::new(&d).fit(x, z), // Weighted case.
        )
        .either_into()
    }
}

impl ParCSSEstimator<CatCPDS> for SSE<'_, CatIncTable> {
    fn par_fit(&self, x: &Set<usize>, z: &Set<usize>) -> CatCPDS {
        // Get the union of X and Z.
        let x_z = Some(&(x | z));
        // Get the missing method or default to PW.
        let m = self.missing_method.as_ref().unwrap_or(&MissingMethod::PW);
        // Get the missing mechanism or default to None.
        let r = self.missing_mechanism.as_ref();

        // Apply the missing handling method.
        let d = self.dataset.apply_missing_method(m, x_z, r);

        // Get the labels of the original dataset.
        let labels = self.dataset.labels();
        // Map the indices from the original dataset to the new one.
        let x = &d.indices_from(x, labels);
        let z = &d.indices_from(z, labels);

        // Estimate based on the resulting dataset in parallel.
        d.map_either(
            |d| SSE::new(&d).par_fit(x, z), // Complete case.
            |d| SSE::new(&d).par_fit(x, z), // Weighted case.
        )
        .either_into()
    }
}