Struct Pq 

pub struct Pq { /* private fields */ }

Potential quantile θ with P(Y(d) ≤ θ) = τ, as DoubleML’s DoubleMLPQ.

ml_g is a classifier for P(Y ≤ θ̃ | D = d, X) at the preliminary estimate θ̃, ml_m a classifier for the propensity P(D = 1 | X). Default sample splits are stratified by the treatment.

Implementations

impl Pq

pub fn new( ml_g: impl Classifier + 'static, ml_m: impl Classifier + 'static, ) -> Self

pub fn from_boxed(ml_g: Box<dyn Classifier>, ml_m: Box<dyn Classifier>) -> Self

pub fn treatment(self, treatment: u8) -> Self

Treatment level d of the potential outcome (0 or 1, default 1).

pub fn quantile(self, quantile: f64) -> Self

Quantile level τ in (0, 1) (default 0.5).

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

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

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

Normalise the inverse-probability weights (default true, as DoubleML).

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

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

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<DmlFit>

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