nuis_func_ai

<p>Nuisance functions generated with `compute_nuisance_functions_ai` for the illustration purpose.</p>

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

nuis_func_ai function

<p>An object of class <code>nuisance_functions</code> of length 2.</p>

nuis_func_ai: Nuisance functions conditioning on AI (internal)

Description

Usage

Arguments

Value

Format