CalDIMsubgroup

Calculate average causal effect based on diff-in-means estimator.

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

CalDIMsubgroup function

<dl><dt>data</dt>
<dd>A <code>data.frame</code> of which columns includes a binary treatment (Z), an ordinal decision (D), and an outcome variable (Y).</dd>
<dt>subgroup</dt>
<dd>A list of numeric vectors for the index of each of the five subgroups.</dd>
<dt>name.group</dt>
<dd>A character vector including the labels of five subgroups.</dd></dl>

Arguments

Calculate diff-in-means estimates — CalDIMsubgroup

<dl>

<dt>data</dt>
<dd>A <code>data.frame</code> of which columns includes a binary treatment (Z), an ordinal decision (D), and an outcome variable (Y).</dd>


<dt>subgroup</dt>
<dd>A list of numeric vectors for the index of each of the five subgroups.</dd>


<dt>name.group</dt>
<dd>A character vector including the labels of five subgroups.</dd>

</dl>

CalDIMsubgroup: Calculate diff-in-means estimates

Description

Usage

Value

Arguments

Examples