Package: aihuman 1.0.0
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> 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.
Authors:
aihuman_1.0.0.tar.gz
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aihuman.pdf |aihuman.html✨
aihuman/json (API)
NEWS
# Install 'aihuman' in R: |
install.packages('aihuman', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/sooahnshin/aihuman/issues
- 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
Last updated 23 days agofrom:b479bfbac9. Checks:2 OK. Indexed: no.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Jan 09 2025 |
R-4.5-linux-x86_64 | OK | Jan 09 2025 |
Exports:A_llamaAiEvalmcmcAPCEsummaryAPCEsummaryipwBootstrapAPCEipwBootstrapAPCEipwREBootstrapAPCEipwREparallelCalAPCECalAPCEipwCalAPCEipwRECalAPCEparallelCalDeltaCalDIMCalDIMsubgroupCalFairnessCalOptimalDecisionCalPScompute_bounds_aipwcompute_nuisance_functionscompute_nuisance_functions_aicompute_statscompute_stats_agreementcompute_stats_aipwcompute_stats_subgroupcrossfitg_legendnca_follow_policynca_follow_policy_decnca_provide_policynca_provide_policy_decnuis_funcnuis_func_aiplot_agreementplot_diff_ai_aipwplot_diff_humanplot_diff_human_aipwplot_diff_subgroupplot_preferencePlotAPCEPlotDIMdecisionsPlotDIMoutcomesPlotFairnessPlotOptimalDecisionPlotPSPlotSpilloverCRTPlotSpilloverCRTpowerPlotStackedBarPlotStackedBarDMFPlotUtilityDiffPlotUtilityDiffCISpilloverCRTSpilloverCRTpowertable_agreementTestMonotonicityTestMonotonicityREvis_agreementvis_diff_aivis_diff_humanvis_diff_subgroupvis_preference
Dependencies:abindbackportscachemcheckmateclassclassIntclicodacodetoolscolorspacecpp11data.tableDBIdigestdoParalleldplyre1071fansifarverfastmapforcatsforeachFormulaformula.toolsgbmgenericsggplot2GLMMadaptivegluegtableisobanditeratorsKernSmoothlabelinglatticelifecyclelubridatemagrittrMASSMatrixmatrixStatsmemoisemetRmgcvmunsellnlmeoperator.toolspillarpkgconfigplyrproxypurrrR6RColorBrewerRcppRcppArmadilloRcppEigenrlangs2scalessfstringistringrsurvivaltibbletidyrtidyselecttimechangeunitsutf8vctrsviridisLitewithrwk
Replication Codes for Does AI help humans make better decisions?
Rendered fromability.Rmd
usingknitr::rmarkdown
on Jan 09 2025.Last update: 2025-01-09
Started: 2025-01-09
Replication Codes for Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment
Rendered fromaihuman.Rmd
usingknitr::rmarkdown
on Jan 09 2025.Last update: 2025-01-09
Started: 2023-03-02