g_legend

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

g_legend function

<dl><dt>p</dt>
<dd>A <code>ggplot</code> of which legend should be pulled.</dd></dl>

Arguments

Pulling ggplot legend — g_legend

<dl>

<dt>p</dt>
<dd>A <code>ggplot</code> of which legend should be pulled.</dd>

</dl>

g_legend: Pulling ggplot legend

Description

Usage

Value

Arguments