Struct DmlData 

pub struct DmlData { /* private fields */ }

Data for a DML model.

• y: outcome, length n
• d: one or more treatments, shape (n, k) (continuous or 0/1)
• x: covariates / confounders, shape (n, p)
• z: optional instrument(s), length n (for Pliv, which also takes several, and, binary, Iivm)
• t: optional 0/1 time-period indicator (for repeated cross-sections, DidCs)
• s: optional 0/1 selection indicator, 1 where the outcome is observed (for Ssm)

With several treatments each one is estimated in turn, and the other treatments are added to the covariates (as in DoubleML).

Implementations

impl DmlData

pub fn new(y: Array1<f64>, d: Array1<f64>, x: Array2<f64>) -> Result<Self>

Single treatment.

pub fn with_treatments( y: Array1<f64>, d: Array2<f64>, x: Array2<f64>, ) -> Result<Self>

Several treatments, one per column of d.

pub fn with_instrument(self, z: Array1<f64>) -> Result<Self>

Attach an instrument z (required by Pliv).

pub fn with_instruments(self, z: Array2<f64>) -> Result<Self>

Attach several instruments, one per column (Pliv combines them; the other instrumental models need exactly one).

pub fn with_time_indicator(self, t: Array1<f64>) -> Result<Self>

Attach a 0/1 time-period indicator (post-period = 1), required by DidCs for repeated cross-sections.

pub fn with_selection_indicator(self, s: Array1<f64>) -> Result<Self>

Attach a 0/1 selection indicator (1 where the outcome is observed), required by the sample selection model Ssm. The outcome of unselected observations is not used but must be finite (e.g. 0).

pub fn with_clusters(self, cluster: Array1<i64>) -> Result<Self>

Cluster the observations (e.g. by firm or school): sample splitting then keeps clusters together and standard errors are cluster-robust.

pub fn with_two_way_clusters( self, first: Array1<i64>, second: Array1<i64>, ) -> Result<Self>

Two-way clustering (e.g. by firm and by year).

pub fn clusters(&self) -> Option<&[Array1<i64>]>

The cluster variables (one or two), if any.

pub fn without_covariates(&self, drop: &[usize]) -> Result<Self>

The same data without the covariate columns drop (for sensitivity benchmarks).

pub fn with_treatment_names<S: Into<String>>( self, names: impl IntoIterator<Item = S>, ) -> Result<Self>

Names shown in summaries (default d, or d0, d1, …).

pub fn n_obs(&self) -> usize

pub fn n_features(&self) -> usize

pub fn n_treat(&self) -> usize

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

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

All treatments, shape (n, k).

pub fn treatment(&self, j: usize) -> ArrayView1<'_, f64>

Treatment j.

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

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

The instrument, if exactly one is attached.

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

All instruments, one per column.

pub fn n_instruments(&self) -> usize

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

The 0/1 time-period indicator, if attached.

pub fn with_external_predictions(self, external: ExternalPredictions) -> Self

Use user-supplied out-of-fold predictions for some nuisance learners instead of fitting them (DoubleML’s external_predictions).

pub fn external_predictions(&self) -> Option<&ExternalPredictions>

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

The 0/1 selection indicator, if attached.

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

pub fn covariates_for(&self, j: usize) -> Cow<'_, Array2<f64>>

Covariates used when estimating the effect of treatment j: x plus the other treatment columns. Borrowed when there is only one treatment.

(Cow = “clone on write”: either a borrowed view or an owned matrix.)

pub fn is_binary_treatment(&self, j: usize) -> bool

True when every value of treatment j is exactly 0 or 1.

Trait Implementations

impl Clone for DmlData

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 DmlData

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

Formats the value using the given formatter.