Module model 

DML models with linear scores: Plr, Irm, Pliv, and the LinearScoreModel trait for adding your own.

Structs

Apo

Average potential outcome E[Y(d)] of one treatment level d.

Apos

Average potential outcomes of several treatment levels, fitted on shared sample splits so that they (and their contrasts) have joint inference.

AposFit

Fitted Apos: the average potential outcome of every level, with joint inference, and contrasts between levels.

CateSignal

Ingredients for best-linear-predictor inference on effect heterogeneity (DmlFit::cate, DmlFit::gate): the BLP regresses signal − θ·theta_factor on the basis, with every row of the basis multiplied by basis_scale if given (partially linear models, where the signal is the outcome residual and the scale the treatment residual).

Cvar

Conditional value at risk of the potential outcome, E[Y(d) | Y(d) ≥ q_τ(Y(d))], as DoubleML’s DoubleMLCVAR. ml_g is a regressor, ml_m a classifier for P(D = 1 | X). The score is linear.

Did

Difference-in-differences with panel data (two periods): y must be the outcome change Y₁ − Y₀. ml_g learns E[ΔY | D = d, X] per group, ml_m the propensity P(D = 1 | X) (observational score only).

DidCs

Difference-in-differences with repeated cross-sections: the data need a 0/1 time indicator (DmlData::with_time_indicator). ml_g learns E[Y | D = d, T = t, X] separately in each of the four group × period cells, ml_m the propensity P(D = 1 | X) (observational score only).

Iivm

IIVM estimator of the LATE: ml_g learns E[Y | Z, X] (fitted separately on the Z = 0 and Z = 1 rows), ml_m the instrument propensity P(Z = 1 | X), and ml_r the treatment probability P(D = 1 | Z, X) (again per instrument arm).

Irm

IRM estimator: ml_g learns E[Y | D = d, X] (fitted separately on the treated and control rows of each training fold), ml_m learns the propensity score P(D = 1 | X).

Lpq

Local potential quantile of compliers, with a binary instrument, as DoubleML’s DoubleMLLPQ. ml_g is a classifier for P(1{D = d}·1{Y ≤ θ̃} = 1 | Z = z, X), ml_m a classifier used for the instrument propensity P(Z = 1 | X) and for P(D = 1 | Z = z, X). Default sample splits are stratified by treatment and instrument.

NuisanceTarget

The target a nuisance learner predicts; NaN marks rows where the nuisance does not apply (e.g. g₀ on treated rows).

Pliv

PLIV estimator with the partialling-out score ψ = (Y − ℓ(X) − θ(D − r(X)))·(Z − m(X)).

Plr

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.

Pq

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

Qte

Treatment effects on quantiles (or CVaRs), as DoubleML’s DoubleMLQTE: for every τ, the potential quantiles of treatment 1 and 0 are fitted on the same sample splits and their difference is the effect, with joint inference across all quantiles.

QteFit

Fitted Qte.

ScoreEstimate

Score elements plus the cross-fitted nuisance predictions behind them.

ScoreInput

The data a model sees when estimating the effect of one treatment.

Enums

DidScore

Assignment mechanism assumed by Did / DidCs.

IrmScore

Target parameter of Irm.

LossKind

Loss used to evaluate a nuisance learner out of sample.

PlrScore

Orthogonal score used by Plr.

Traits

LinearScoreModel

A DML model whose orthogonal score is linear in θ.