Struct Qte 

pub struct Qte { /* private fields */ }

Treatment effects on quantiles (or CVaRs), as DoubleML’s DoubleMLQTE: for every τ, the potential quantiles of treatment 1 and 0 are fitted on the same sample splits and their difference is the effect, with joint inference across all quantiles.

Implementations

impl Qte

pub fn pq( ml_g: impl Classifier + 'static, ml_m: impl Classifier + 'static, quantiles: Vec<f64>, ) -> Self

Effects on quantiles (potential quantiles, Pq).

pub fn lpq( ml_g: impl Classifier + 'static, ml_m: impl Classifier + 'static, quantiles: Vec<f64>, ) -> Self

Effects on compliers’ quantiles (local potential quantiles, Lpq).

pub fn cvar( ml_g: impl Regressor + 'static, ml_m: impl Classifier + 'static, quantiles: Vec<f64>, ) -> Self

Effects on the conditional value at risk (Cvar).

pub fn from_boxed( score: &str, ml_g_classifier: Option<Box<dyn Classifier>>, ml_g_regressor: Option<Box<dyn Regressor>>, ml_m: Box<dyn Classifier>, quantiles: Vec<f64>, ) -> Result<Self>

Boxed learners, e.g. from Python: score is "PQ", "LPQ" or "CVaR"; for "CVaR" pass ml_g_regressor, otherwise ml_g_classifier.

pub fn clipping_threshold(self, t: f64) -> Self

Propensity scores are clipped to [t, 1 − t] (default t = 0.01).

pub fn calibration(self, calibration: Calibration) -> Self

Calibrate the propensity scores before clipping (default: none), as DoubleML’s ps_processor_config.

pub fn normalize_ipw(self, normalize: bool) -> Self

Normalise the inverse-probability weights (default true).

pub fn n_folds(self, n_folds: usize) -> Self

Number of cross-fitting folds (default 5).

pub fn n_rep(self, n_rep: usize) -> Self

Number of independent sample splits (default 1).

pub fn seed(self, seed: u64) -> Self

Seed for the sample splits (default 0).

pub fn fit(&self, data: &DmlData) -> Result<QteFit>

Draw sample splits (stratified by the treatment) and fit.

pub fn fit_with_folds( &self, data: &DmlData, splits: &[Vec<Fold>], ) -> Result<QteFit>

Fit on given sample splits (one Vec<Fold> per repetition).