aihuman 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).
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.
You can install the development version of aihuman from GitHub with:
# install.packages("devtools")
devtools::install_github("sooahnshin/aihuman")The main functions in this package are as follows:
| Category | Function | Type | Main Input | Output | Paper | Notes |
|---|---|---|---|---|---|---|
| descriptive | PlotStackedBar() |
vis. | e.g., data(psa_synth) |
ggplot | Fig 1 | dist. of \(D_i\) |
| descriptive | CalDIMsubgroup() |
est. | e.g., data(synth) |
dataframe | Sec 2.4 | diff-in-means |
| descriptive | PlotDIMdecisions() |
vis. | output of
CalDIMsubgroup() |
ggplot | Fig 2 (left) | |
| descriptive | PlotDIMoutcomes() |
vis. | output of CalDIMsubgroup()
for each outcome |
ggplot | Fig 2 (right) | |
| main | AiEvalmcmc() |
est.(c/h) | e.g., data(synth) |
mcmc | Sec S5 | |
| main | CalAPCE() |
est.(c/h) | output of PSAmcmc() |
list | Sec 3.4 | |
| main | APCEsummary() |
est. | output of CalAPCE() |
dataframe | Sec 3.4 | APCE |
| main | PlotAPCE() |
vis. | output of APCEsummary() |
ggplot | Fig 4 | |
| strata | CalPS() |
est. | $P.R.mcmc of
CalAPCE() |
dataframe | Eq 6 | \(e_r\) |
| strata | PlotPS() |
vis. | output of CalPS() |
ggplot | Fig 3 | |
| fairness | CalFairness() |
est. | output of CalAPCE() |
dataframe | Sec 3.6 | \(\Delta_r(z)\) |
| fairness | PlotFairness() |
est. | output of CalFairness() |
ggplot | Fig 5 | |
| optimal | CalOptimalDecision() |
est. | output of AiEvalmcmc() |
dataframe | Sec 3.7 | \(\delta^\ast(\mathbf{x})\) |
| optimal | PlotOptimalDecision() |
vis. | output of
CalOptimalDecision() |
ggplot | Fig 6 | |
| comparison | PlotUtilityDiff() |
vis. | output of
CalOptimalDecision() |
ggplot | Fig 7 | \(g_d(\mathbf{x})\) |
| comparison | PlotUtilityDiffCI() |
vis. | output of
CalOptimalDecision() |
ggplot | Fig S17 | |
| crt | SpilloverCRT() |
est. | court event hearing date, \(D_i\), \(Z_i\) | daraframe | Sec S3.1 | |
| crt | PlotSpilloverCRT() |
vis. | output of SpilloverCRT() |
ggplot | Fig S8 | |
| crt power | SpilloverCRTpower() |
est. | court event hearing date, \(D_i\), \(Z_i\) | dataframe | Sec S3.2 | |
| crt power | PlotSpilloverCRTpower() |
vis. | output of
SpilloverCRTpower() |
ggplot | Fig S9 | |
| frequentist | CalAPCEipw() |
est. | e.g., data(synth) |
dataframe | Sec S7 | |
| frequentist | BootstrapAPCEipw() |
est. | e.g., data(synth) |
dataframe | ||
| frequentist | APCEsummaryipw() |
est. | outputs of CalAPCEipw() and
BootstrapAPCEipw() |
dataframe | ||
| frequentist (RE) | CalAPCEipwRE() |
est. | e.g., data(synth) |
dataframe | ||
| frequentist (RE) | BootstrapAPCEipwRE() |
est. | e.g., data(synth) |
dataframe |
vis. = visualization; est. = estimation; c/h = computation-heavy. You
may use CalAPCEparallel() instead of CalAPCE()
throughout the analysis.
For more details see the aihuman package vignette,
vignette("aihuman", package = "aihuman").
library(aihuman)
## Using synthetic data with small run
data(synth)
sample_mcmc = AiEvalmcmc(data = synth, n.mcmc = 10)
#> 10/10 done.
subgroup_synth = list(1:nrow(synth),
which(synth$Sex==0),
which(synth$Sex==1),
which(synth$Sex==1&synth$White==0),
which(synth$Sex==1&synth$White==1))
sample_apce = CalAPCE(data = synth,
mcmc.re = sample_mcmc,
subgroup = subgroup_synth)
# You can also use the parallelized version: check CalAPCEparallel()
sample_apce_summary = APCEsummary(sample_apce[["APCE.mcmc"]])
PlotAPCE(sample_apce_summary,
y.max = 0.25,
decision.labels = c("signature","small cash","middle cash","large cash"),
shape.values = c(16, 17, 15, 18),
col.values = c("blue", "black", "red", "brown", "purple"),
label = FALSE)