prep_cf_mat

Creates matrix of binary cross-fitting fold indicators (N x # cross-folds)

Many treatment effect estimators can be written as weighted outcomes.
These weights have established use cases like checking covariate balancing via packages like 'cobalt'.
This package takes the original estimator objects and outputs these outcome weights.
It builds on the general framework of Knaus (2024) <doi:10.48550/arXiv.2411.11559>.
This version is compatible with the 'grf' package and provides an internal implementation of Double Machine Learning.

Knaus Michael C.

OutcomeWeights

Outcome Weights of Treatment Effect Estimators

Michael C. Knaus

Henri Pfleiderer

prep_cf_mat function

<dl><dt>n</dt>
<dd>Number of observations.</dd>
<dt>cf</dt>
<dd>Number of cross-fitting folds.</dd>
<dt>w_mat</dt>
<dd>Optional logical matrix of treatment indicators (N x T+1).
If specified, cross-fitting folds will preserve the treatment ratios from full sample.</dd>
<dt>cl</dt>
<dd>Optional vector of cluster variable if cross-fitting should account for clusters.</dd></dl>

Arguments

Creates matrix of binary cross-fitting fold indicators (N x # cross-folds) — prep_cf_mat

<dl>

<dt>n</dt>
<dd>Number of observations.</dd>


<dt>cf</dt>
<dd>Number of cross-fitting folds.</dd>


<dt>w_mat</dt>
<dd>Optional logical matrix of treatment indicators (N x T+1).
If specified, cross-fitting folds will preserve the treatment ratios from full sample.</dd>


<dt>cl</dt>
<dd>Optional vector of cluster variable if cross-fitting should account for clusters.</dd>

</dl>

prep_cf_mat: Creates matrix of binary cross-fitting fold indicators (N x # cross-folds)

Description

Usage

Value

Arguments