Struct Plpr 

pub struct Plpr { /* private fields */ }

Partially linear regression on a transformed static panel, with folds and standard errors clustered by unit. See PanelApproach for the transformations and PlrScore for the scores.

Implementations

impl Plpr

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

ml_l learns E[Y | X], ml_m E[D | X] on the transformed panel.

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

ml_m may be a classifier for a binary treatment with the correlated random effects approaches (the other approaches transform the treatment, which is then no longer binary).

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

pub fn approach(self, approach: PanelApproach) -> Self

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

Learner for E[Y − θD | X] with the IV-type score (default: a 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), over units.

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

Number of independent sample splits (default 1), aggregated by the median.

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

Seed for the sample splits (default 0).

pub fn transform(&self, panel: &StaticPanel) -> Result<DmlData>

The transformed panel the partially linear regression is fitted on, clustered by unit.

pub fn fit(&self, panel: &StaticPanel) -> Result<DmlFit>

Draw folds of whole units and fit.

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

Fit on given folds of the transformed rows (Plpr::transform); each test set should hold whole units.

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

Fit on given cluster folds of the transformed rows.