Struct DmlFit 

pub struct DmlFit { /* private fields */ }

A fitted DML model: estimates, standard errors and the per-repetition scores and nuisance predictions behind them.

Arrays indexed by treatment have length k = n_treat; per-repetition arrays have shape (k, n_rep).

Implementations

impl DmlFit

pub fn bootstrap( &self, method: BootstrapMethod, n_boot: usize, seed: u64, ) -> Result<Bootstrap>

Multiplier bootstrap with n_boot draws per repetition: t*_b = Σ_i ξ_bi · ψ_i / (J · n · se).

pub fn bootstrap_with_weights( &self, weights: &[Array2<f64>], ) -> Result<Bootstrap>

Bootstrap with caller-supplied weights, one (n_boot, n) matrix per repetition (e.g. to reproduce DoubleML’s draws exactly).

impl DmlFit

pub fn model_name(&self) -> &str

pub fn treatment_names(&self) -> &[String]

pub fn n_obs(&self) -> usize

pub fn n_treat(&self) -> usize

pub fn n_rep(&self) -> usize

pub fn n_folds(&self) -> usize

pub fn coef(&self) -> ArrayView1<'_, f64>

Point estimates θ̂ (median over repetitions).

pub fn se(&self) -> ArrayView1<'_, f64>

Standard errors (aggregated over repetitions).

pub fn t_stat(&self) -> Array1<f64>

pub fn p_value(&self) -> Array1<f64>

Two-sided p-values for H0: θ = 0 from the aggregated t-statistic, as DoubleML’s pval. (Multiple-testing adjustments in p_adjust instead take the median over repetitions.)

pub fn confint(&self, level: f64) -> Result<Array2<f64>>

Pointwise Wald confidence intervals at level, shape (k, 2) (median over repetitions of each repetition’s interval).

pub fn p_adjust(&self, method: PAdjust) -> Array1<f64>

Multiple-testing adjusted p-values across treatments (applied per repetition, then the median is taken). For the bootstrap-based Romano–Wolf procedure see Bootstrap::romano_wolf.

pub fn all_coef(&self) -> ArrayView2<'_, f64>

Estimates for every repetition, shape (k, n_rep).

pub fn all_se(&self) -> ArrayView2<'_, f64>

pub fn psi(&self) -> ArrayView3<'_, f64>

Score values ψ at θ̂, shape (n, k, n_rep).

pub fn psi_elements(&self) -> (ArrayView3<'_, f64>, ArrayView3<'_, f64>)

Score elements ψ_a and ψ_b (ψ = ψ_a·θ + ψ_b), shape (n, k, n_rep).

pub fn cate(&self, basis: ArrayView2<'_, f64>) -> Result<Blp>

Best linear predictor of the CATE on basis (shape (n, p), include a constant column for an intercept), using the orthogonal signal ψ_b. Valid for scores with ψ_a = −1, i.e. Irm with the ATE score.

pub fn gate(&self, groups: ArrayView2<'_, f64>) -> Result<Blp>

Group average treatment effects for mutually exclusive groups given as 0/1 dummy columns (shape (n, n_groups)).

pub fn nuisance_names(&self) -> impl Iterator<Item = &str>

Names of the stored nuisance predictions (e.g. ml_l, ml_m).

pub fn predictions(&self, name: &str) -> Option<ArrayView3<'_, f64>>

All predictions of one nuisance, shape (n, n_rep, k).

pub fn prediction( &self, name: &str, rep: usize, treatment: usize, ) -> Option<Array1<f64>>

Out-of-fold predictions of one nuisance for a repetition and treatment.

Trait Implementations

impl Clone for DmlFit

fn clone(&self) -> Self

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for DmlFit

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Display for DmlFit

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.