crossfit

Implement crossfitting with boosting methods and get predicted values for outcome/decision regression or propensity score models

Provides statistical methods for analyzing experimental
evaluation of the causal impacts of algorithmic recommendations
on human decisions developed by Imai, Jiang, Greiner, Halen, and
Shin (2023) <doi:10.1093/jrsssa/qnad010> and Ben-Michael, Greiner,
Huang, Imai, Jiang, and Shin (2024) <doi:10.48550/arXiv.2403.12108>.
The data used for this paper, and made available here, are interim,
based on only half of the observations in the study and (for those
observations) only half of the study follow-up period. We use them
only to illustrate methods, not to draw substantive conclusions.

Sooahn Shin

aihuman

Experimental Evaluation of Algorithm-Assisted Human
Decision-Making

Zhichao Jiang

Kosuke Imai

crossfit function

<dl><dt>data</dt>
<dd>A <code>data.frame</code> or <code>matrix</code> to fit on.</dd>
<dt>include_for_fit</dt>
<dd>Boolean vector for whether or not a unit should be included in fitting (e.g. treated/control).</dd>
<dt>form</dt>
<dd>Formula for outcome regression/propensity score models.</dd>
<dt>...</dt>
<dd>Additional arguments to be passed to <code>gbm</code> function.</dd></dl>

Arguments

Crossfitting for nuisance functions — crossfit

<dl>

<dt>data</dt>
<dd>A <code>data.frame</code> or <code>matrix</code> to fit on.</dd>


<dt>include_for_fit</dt>
<dd>Boolean vector for whether or not a unit should be included in fitting (e.g. treated/control).</dd>


<dt>form</dt>
<dd>Formula for outcome regression/propensity score models.</dd>


<dt>...</dt>
<dd>Additional arguments to be passed to <code>gbm</code> function.</dd>

</dl>

crossfit: Crossfitting for nuisance functions

Description

Usage

Value

Arguments