aihuman-package {aihuman}

Experimental Evaluation of Algorithm-Assisted Human Decision-Making

Description

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>. 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.

Package Content

Index of help topics:

APCEsummary             Summary of APCE
APCEsummaryipw          Summary of APCE for frequentist analysis
AiEvalmcmc              Gibbs sampler for the main analysis
BootstrapAPCEipw        Bootstrap for estimating variance of APCE
BootstrapAPCEipwRE      Bootstrap for estimating variance of APCE with
                        random effects
BootstrapAPCEipwREparallel
                        Bootstrap for estimating variance of APCE with
                        random effects
CalAPCE                 Calculate APCE
CalAPCEipw              Compute APCE using frequentist analysis
CalAPCEipwRE            Compute APCE using frequentist analysis with
                        random effects
CalAPCEparallel         Calculate APCE using parallel computing
CalDIM                  Calculate diff-in-means estimates
CalDIMsubgroup          Calculate diff-in-means estimates
CalDelta                Calculate the delta given the principal stratum
CalFairness             Calculate the principal fairness
CalOptimalDecision      Calculate optimal decision & utility
CalPS                   Calculate the proportion of principal strata
                        (R)
FTAdata                 Interim Dane data with failure to appear (FTA)
                        as an outcome
HearingDate             Interim court event hearing date
NCAdata                 Interim Dane data with new criminal activity
                        (NCA) as an outcome
NVCAdata                Interim Dane data with new violent criminal
                        activity (NVCA) as an outcome
PSAdata                 Interim Dane PSA data
PlotAPCE                Plot APCE
PlotDIMdecisions        Plot diff-in-means estimates
PlotDIMoutcomes         Plot diff-in-means estimates
PlotFairness            Plot the principal fairness
PlotOptimalDecision     Plot optimal decision
PlotPS                  Plot the proportion of principal strata (R)
PlotSpilloverCRT        Plot conditional randomization test
PlotSpilloverCRTpower   Plot power analysis of conditional
                        randomization test
PlotStackedBar          Stacked barplot for the distribution of the
                        decision given psa
PlotStackedBarDMF       Stacked barplot for the distribution of the
                        decision given DMF recommendation
PlotUtilityDiff         Plot utility difference
PlotUtilityDiffCI       Plot utility difference with 95
                        interval
SpilloverCRT            Conduct conditional randomization test
SpilloverCRTpower       Conduct power analysis of conditional
                        randomization test
TestMonotonicity        Test monotonicity
TestMonotonicityRE      Test monotonicity with random effects
aihuman-package         Experimental Evaluation of Algorithm-Assisted
                        Human Decision-Making
g_legend                Pulling ggplot legend
hearingdate_synth       Synthetic court event hearing date
psa_synth               Synthetic PSA data
synth                   Synthetic data

Maintainer

NA

Author(s)

NA