Struct Plr 

pub struct Plr { /* private fields */ }

PLR estimator: ml_l learns E[Y | X], ml_m learns E[D | X], and the optional ml_g learns g(X) for the IV-type score.

Implementations

impl Plr

pub fn new( ml_l: impl Regressor + 'static, ml_m: impl Regressor + 'static, ) -> Self

pub fn from_boxed(ml_l: Box<dyn Regressor>, ml_m: Box<dyn Regressor>) -> Self

Like Plr::new for learners that are already boxed trait objects.

pub fn score(self, score: PlrScore) -> Self

pub fn ml_g(self, ml_g: impl Regressor + 'static) -> Self

Learner for g(X) in the IV-type score (default: a fresh copy of ml_l).

pub fn ml_g_boxed(self, ml_g: Box<dyn Regressor>) -> Self

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). Estimates are aggregated across repetitions by the median, as in DoubleML.

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

Seed for the sample splits (default 0).

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

Draw seeded sample splits and fit. With clustered data (DmlData::with_clusters) whole clusters are assigned to folds and standard errors are cluster-robust.

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

Fit clustered data with explicit cluster folds, one Vec<ClusterFold> per repetition.

pub fn sensitivity_benchmark( &self, data: &DmlData, long: &DmlFit, benchmarking_set: &[usize], ) -> Result<SensitivityBenchmark>

Sensitivity benchmark for the covariate columns benchmarking_set: refits this model without them (with the same sample splitting, so long must come from fit with the current settings) and compares it with long.

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

Fit with caller-supplied splits, one Vec<Fold> per repetition (e.g. to reproduce another implementation exactly).

Trait Implementations

impl LinearScoreModel for Plr

fn name(&self) -> String

Label for summaries, e.g. "PLR (partialling out)".

fn estimate_score( &self, input: &ScoreInput<'_>, folds: &[Fold], ) -> Result<ScoreEstimate>

Cross-fit the nuisances on folds and return the score elements.

fn validate(&self, _data: &DmlData) -> Result<()>

Reject data the model cannot handle (called once before fitting).

fn strata(&self, _data: &DmlData) -> Option<Vec<usize>>

Strata for the default sample splits (e.g. treatment levels), so each fold holds about the same share of every stratum. None (the default) draws plain K-fold splits.