nca_follow_policy

Optimal policy whether to follow PSA recommendation for NCA as an outcome
and policy class with an increasing monotonicity constraint.
Used for the replication in the vignette.

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

nca_follow_policy function

A data frame with 41 rows and 6 variables:<dl>
 <dt>FTAScore</dt>
<dd>FTA score</dd> <dt>NCAScore</dt>
<dd>NCA score</dd> <dt>NVCAFlag</dt>
<dd>NVCA flag</dd> <dt>wts</dt>
<dd>Weight for each observation</dd> <dt>n</dt>
<dd>Number of such cases</dd> <dt>policy</dt>
<dd>Optimal policy</dd>
</dl>

Format

NCA follow policy (internal; increasing monotonicity) — nca_follow_policy

A data frame with 41 rows and 6 variables:<dl>
 <dt>FTAScore</dt>
<dd>FTA score</dd>

 <dt>NCAScore</dt>
<dd>NCA score</dd>

 <dt>NVCAFlag</dt>
<dd>NVCA flag</dd>

 <dt>wts</dt>
<dd>Weight for each observation</dd>

 <dt>n</dt>
<dd>Number of such cases</dd>

 <dt>policy</dt>
<dd>Optimal policy</dd>


</dl>

nca_follow_policy: NCA follow policy (internal; increasing monotonicity)

Description

Usage

Arguments

Format