vis_agreement

Internal function to visualize the agreement between human decisions and AI recommendations using a difference-in-means estimator of an indicator \(1\{D_i = A_i\}\).

internal

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

vis_agreement function

<dl><dt>df</dt>
<dd>A data frame generated by <code>compute_stats_agreement</code> and reshaped.</dd>
<dt>p.title</dt>
<dd>A title for the plot (character). Default NULL.</dd>
<dt>p.lb</dt>
<dd>A lower bound for the y-axis (numeric). Default -0.2.</dd>
<dt>p.ub</dt>
<dd>An upper bound for the y-axis (numeric). Default 0.2.</dd>
<dt>y.lab</dt>
<dd>A label for the y-axis (character). Default "Impact of PSA".</dd></dl>

Arguments

Visualize Agreement (internal) — vis_agreement

<dl>

<dt>df</dt>
<dd>A data frame generated by <code>compute_stats_agreement</code> and reshaped.</dd>


<dt>p.title</dt>
<dd>A title for the plot (character). Default NULL.</dd>


<dt>p.lb</dt>
<dd>A lower bound for the y-axis (numeric). Default -0.2.</dd>


<dt>p.ub</dt>
<dd>An upper bound for the y-axis (numeric). Default 0.2.</dd>


<dt>y.lab</dt>
<dd>A label for the y-axis (character). Default "Impact of PSA".</dd>

</dl>

vis_agreement: Visualize Agreement (internal)

Description

Usage

Value

Arguments