Package: aihuman 0.1.1

aihuman: Experimental Evaluation of Algorithm-Assisted Human Decision-Making

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.

Authors:Sooahn Shin [aut, cre], Zhichao Jiang [aut], Kosuke Imai [aut]

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aihuman.pdf |aihuman.html✨
aihuman/json (API)
NEWS

# Install 'aihuman' in R:
install.packages('aihuman', repos = c('https://sooahnshin.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/sooahnshin/aihuman/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • 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
  • hearingdate_synth - Synthetic court event hearing date
  • psa_synth - Synthetic PSA data
  • synth - Synthetic data

On CRAN:

33 exports 1 stars 0.84 score 73 dependencies 4 scripts 232 downloads

Last updated 9 months agofrom:fe1786b42c. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 27 2024
R-4.5-win-x86_64OKAug 27 2024
R-4.5-linux-x86_64OKAug 27 2024
R-4.4-win-x86_64OKAug 27 2024
R-4.4-mac-x86_64OKAug 27 2024
R-4.4-mac-aarch64OKAug 27 2024
R-4.3-win-x86_64OKAug 27 2024
R-4.3-mac-x86_64OKAug 27 2024
R-4.3-mac-aarch64OKAug 27 2024

Exports:AiEvalmcmcAPCEsummaryAPCEsummaryipwBootstrapAPCEipwBootstrapAPCEipwREBootstrapAPCEipwREparallelCalAPCECalAPCEipwCalAPCEipwRECalAPCEparallelCalDeltaCalDIMCalDIMsubgroupCalFairnessCalOptimalDecisionCalPSg_legendPlotAPCEPlotDIMdecisionsPlotDIMoutcomesPlotFairnessPlotOptimalDecisionPlotPSPlotSpilloverCRTPlotSpilloverCRTpowerPlotStackedBarPlotStackedBarDMFPlotUtilityDiffPlotUtilityDiffCISpilloverCRTSpilloverCRTpowerTestMonotonicityTestMonotonicityRE

Dependencies:abindbackportsbootcachemcheckmateclassclassIntclicodacodetoolscolorspacecpp11data.tableDBIdigestdoParalleldplyre1071fansifarverfastmapforeachFormulaformula.toolsgenericsggplot2gluegtableisobanditeratorsKernSmoothlabelinglatticelifecyclelme4lubridatemagrittrMASSMatrixmemoisemetRmgcvminqamunsellnlmenloptroperator.toolspillarpkgconfigplyrproxypurrrR6RColorBrewerRcppRcppArmadilloRcppEigenrlangs2scalessfstringistringrtibbletidyrtidyselecttimechangeunitsutf8vctrsviridisLitewithrwk

Replication Codes for Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment

Sooahn Shin

Rendered fromaihuman.Rmdusingknitr::rmarkdownon Aug 27 2024.

Last update: 2023-02-25
Started: 2023-02-25

Readme and manuals

Help Manual

Help pageTopics
Experimental Evaluation of Algorithm-Assisted Human Decision-Makingaihuman-package aihuman
Gibbs sampler for the main analysisAiEvalmcmc
Summary of APCEAPCEsummary
Summary of APCE for frequentist analysisAPCEsummaryipw
Bootstrap for estimating variance of APCEBootstrapAPCEipw
Bootstrap for estimating variance of APCE with random effectsBootstrapAPCEipwRE
Bootstrap for estimating variance of APCE with random effectsBootstrapAPCEipwREparallel
Calculate APCECalAPCE
Compute APCE using frequentist analysisCalAPCEipw
Compute APCE using frequentist analysis with random effectsCalAPCEipwRE
Calculate APCE using parallel computingCalAPCEparallel
Calculate the delta given the principal stratumCalDelta
Calculate diff-in-means estimatesCalDIM
Calculate diff-in-means estimatesCalDIMsubgroup
Calculate the principal fairnessCalFairness
Calculate optimal decision & utilityCalOptimalDecision
Calculate the proportion of principal strata (R)CalPS
Interim Dane data with failure to appear (FTA) as an outcomeFTAdata
Pulling ggplot legendg_legend
Interim court event hearing dateHearingDate
Synthetic court event hearing datehearingdate_synth
Interim Dane data with new criminal activity (NCA) as an outcomeNCAdata
Interim Dane data with new violent criminal activity (NVCA) as an outcomeNVCAdata
Plot APCEPlotAPCE
Plot diff-in-means estimatesPlotDIMdecisions
Plot diff-in-means estimatesPlotDIMoutcomes
Plot the principal fairnessPlotFairness
Plot optimal decisionPlotOptimalDecision
Plot the proportion of principal strata (R)PlotPS
Plot conditional randomization testPlotSpilloverCRT
Plot power analysis of conditional randomization testPlotSpilloverCRTpower
Stacked barplot for the distribution of the decision given psaPlotStackedBar
Stacked barplot for the distribution of the decision given DMF recommendationPlotStackedBarDMF
Plot utility differencePlotUtilityDiff
Plot utility difference with 95% confidence intervalPlotUtilityDiffCI
Synthetic PSA datapsa_synth
Interim Dane PSA dataPSAdata
Conduct conditional randomization testSpilloverCRT
Conduct power analysis of conditional randomization testSpilloverCRTpower
Synthetic datasynth
Test monotonicityTestMonotonicity
Test monotonicity with random effectsTestMonotonicityRE