Package 'dsld'

Criminal Offenders Screened in Florida

Description

A collection of criminal offenders screened in Florida (US) during 2013-14. This data was used to predict recidivism.

Additional details for this dataset can be found via the fairml package.

---

dsldBnlearn

Description

Wrappers for functions in the bnlearn package. (Just (Presently, just iamb.)

Usage

dsldIamb(data)

Arguments

data	Data frame.

Details

Under very stringent assumptions, dsldIamb performs causal discovery, i.e. fits a causal model to data.

Value

Object of class 'bn' (bnlearn object). The generic plot function is callable on this object.

Author(s)

N. Matloff

Examples

data(svcensus)
   # iamb does not accept integer data
   svcensus$wkswrkd <- as.numeric(svcensus$wkswrkd)
   svcensus$wageinc <- as.numeric(svcensus$wageinc)
   iambOut <- dsldIamb(svcensus)
   plot(iambOut)

---

Confounder and Proxy Hunting

Description

Confounder hunting: searches for variables C that predict both Y and S. Proxy hunting: searches for variables O that predict S.

Usage

dsldCHunting(data,yName,sName,intersectDepth=10)
dsldOHunting(data,yName,sName)

Arguments

data	Data frame.
yName	Name of the response variable column.
sName	Name of the sensitive attribute column.
intersectDepth	Maximum size of intersection of the Y predictor set and the S predictor set

Details

dsldCHunting: The random forests function qeML:qeRF will be run on the indicated data to indicate feature importance in prediction of Y (without S) and S (without Y). Call these "important predictors" of Y and S.

Then for each i from 1 to intersectDepth, the intersection of the top i important predictors of Y and the the top i important predictors of S will be reported, thus suggesting possible confounders. Larger values of i will report more potential confounders, though including progressively weaker ones.

The analyst then may then consider omitting the variables C from models of the effect of S on Y.

Note: Run times may be long.

dsldOHunting: Factors, if any, will be converted to dummy variables, and then the Kendall Tau correlations will be calculated betwene S and potential proxy variables O, i.e. every column other than Y and S. (The Y column itself doesn't enter into computation.)

In fairness analyses, in which one desires to either eliminate or reduce the impact of S, one must consider the indirect effect of S via O. One may wish to eliminate or reduce the role of O.

Value

The function dsldCHunting returns an R list, one component for each confounder set found.

The function dsldOHunting returns an R matrix of correlations, one row for each level of S.

Author(s)

N. Matloff

Examples

data(lsa) 
dsldCHunting(lsa,'bar','race1')
# e.g. suggests confounders 'decile3', 'lsat'
    
data(mortgageSE)
dsldOHunting(mortgageSE,'deny','black')
# e.g. suggests using loan value and condo purchase as proxies

---

dsldConditDisparity

Description

Plots (estimated) mean Y against X, separately for each level of S, with restrictions condits. May reveal Simpson's Paradox-like differences not seen in merely plotting mean Y against X.

Usage

dsldConditDisparity(data, yName, sName, xName, condits = NULL,
    qeFtn = qeKNN, minS = 50, useLoess = TRUE)

Arguments

data	Data frame or equivalent.
yName	Name of predicted variable Y. Must be numeric or dichtomous R factor.
sName	Name of the sensitive variable S, an R factor
xName	Name of a numeric column for the X-axis.
condits	An R vector; each component is a character string for an R logical expression representing a desired condition involving names(data) other than S and Y.
qeFtn	qeML predictive function (not quoted; only default arguments will be used.)
minS	Minimum size for an S group to be retained in the analysis.
useLoess	If TRUE, do loess smoothing on the fitted regression values.

Value

No value; plot.

Author(s)

N. Matloff, A. Ashok, S. Martha, A. Mittal

Examples

data(compas)
# graph probability of recidivism by race given age, among those with at
# most 4 prior convictions and COMPAS decile score at least 6
compas$two_year_recid <- as.numeric(compas$two_year_recid == "Yes")
dsldConditDisparity(compas,"two_year_recid", "race", "age", 
    c("priors_count <= 4","decile_score>=6"), qeKNN)

dsldConditDisparity(compas,"two_year_recid", "race", "age",
    "priors_count == 0", qeGBoost)

---

dsldConfounders

Description

Plots estimated densities of all continuous features X, conditioned on a specified categorical feature C.

Usage

dsldConfounders(data, sName, graphType = "plotly", fill = FALSE)

Arguments

data	Dataframe, at least 2 columns.
sName	Name of the categorical column, an R factor. In discrimination contexts, Typically a sensitive variable.
graphType	Either "plot" or "plotly", for static or interactive graphs. The latter requires the plotly package.
fill	Only applicable to graphType = "plot" case. Setting to true will color each line down to the x-axis.

Value

No value; plot.

Author(s)

N. Matloff, T. Abdullah, A. Ashok, J. Tran

Examples

data(svcensus)
dsldConfounders(svcensus, "educ")

---

dsldDensityByS

Description

Graphs densities of a response variable, grouped by a sensitive variable. Similar to dsldConfounders, but includes sliders to control the bandwidth of the density estimate (analogous to controlling the bin width in a histogram).

Usage

dsldDensityByS(data, cName, sName, graphType = "plotly", fill = FALSE)

Arguments

data	Datasetwith at least 1 numerical column and 1 factor column
cName	Possible confounding variable column, an R numeric
sName	Name of the sensitive variable column, an R factor
graphType	Type of graph created. Defaults to "plotly".
fill	To fill the graph. Defaults to "FALSE".

Value

No value; plot.

Author(s)

N. Matloff, T. Abdullah, A. Ashok, J. Tran

Examples

data(svcensus)
dsldDensityByS(svcensus, cName = "wageinc", sName = "educ")

---

dsldEDFFair Wrappers

Description

Explicitly Deweighted Features: control the effect of proxies related to sensitive variables for prediction.

Usage

dsldQeFairKNN(data, yName, sNames, deweightPars=NULL, yesYVal=NULL,k=25,
  scaleX=TRUE, holdout=floor(min(1000,0.1*nrow(data))))
dsldQeFairRF(data,yName,sNames,deweightPars=NULL, nTree=500, minNodeSize=10,
  mtry = floor(sqrt(ncol(data))),yesYVal=NULL,
  holdout=floor(min(1000,0.1*nrow(data))))
dsldQeFairRidgeLin(data, yName, sNames, deweightPars = NULL, 
  holdout=floor(min(1000,0.1*nrow(data))))
dsldQeFairRidgeLog(data, yName, sNames, deweightPars = NULL, holdout =
  floor(min(1000, 0.1 * nrow(data))), yesYVal = levels(data[, yName])[2])
## S3 method for class 'dsldQeFair'
predict(object,newx,...)

Arguments

data	Dataframe, training set.
yName	Name of the response variable column.
sNames	Name(s) of the sensitive attribute column(s).
deweightPars	Values for de-emphasizing variables in a split, e.g. 'list(age=0.2,gender=0.5)'. In the linear case, larger values means more deweighting, i.e. less influence of the given variable on predictions. For KNN and random forests, smaller values mean more deweighting.
scaleX	Scale the features. Defaults to TRUE.
yesYVal	Y value to be considered "yes," to be coded 1 rather than 0.
k	Number of nearest neighbors. In functions other than dsldQeFairKNN for which this is an argument, it is the number of neighbors to use in finding conditional probabilities via knnCalib.
holdout	How many rows to use as the holdout/testing set. Can be NULL. The testing set is used to calculate s correlation and test accuracy.
nTree	Number of trees.
minNodeSize	Minimum number of data points in a tree node.
mtry	Number of variables randomly tried at each split.
object	An object returned by the dsld-EDFFAIR wrapper.
newx	New data to be predicted. Must be in the same format as original data.
...	Further arguments.

Details

The sensitive variables S are removed entirely, but there is concern that they still affect prediction indirectly, via a set C of proxy variables.

Linear EDF reduces the impact of the proxies through a shinkage process similar to that of ridge regression. Specifically, instead of minimizing the sum of squared errors SSE with respect to a coefficient vector b, we minimize SSE + the squared norm of Db, where D is a diagonal matrix with nonzero elements corresponding to C. Large values penalizing variables in C, thus shrinking them.

KNN EDF reduces the weights in Euclidean distance for variables in C. The random forests version reduces the probabilities that a proxy will be used in splitting a node.

By using various values of the deweighting parameters, the user can choose a desired position in the Fairness-Utility Tradeoff.

More details can be found in the references.

Value

The EDF functions return objects of class 'dsldQeFair', which include components for test and base accuracy, summaries of inputs and so on.

Author(s)

N. Matloff, A. Mittal, J. Tran

References

https://github.com/matloff/EDFfair

See Also

Matloff, Norman, and Wenxi Zhang. "A novel regularization approach to fair ML."
arXiv preprint arXiv:2208.06557 (2022).

Examples

data(compas1) 
data(svcensus) 

# dsldQeFairKNN: deweight "decile score" column with "race" as 
# the sensitive variable
knnOut <- dsldQeFairKNN(compas1, "two_year_recid", "race", 
   list(decile_score=0.1), yesYVal = "Yes")
knnOut$testAcc 
knnOut$corrs 
predict(knnOut, compas1[1,-8]) 

# dsldFairRF: deweight "decile score" column with "race" as sensitive variable
rfOut <- dsldQeFairRF(compas1, "two_year_recid", "race", 
   list(decile_score=0.3), yesYVal = "Yes")
rfOut$testAcc
rfOut$corrs 
predict(rfOut, compas1[1,-8]) 

# dsldQeFairRidgeLin: deweight "occupation" and "age" columns
lin <- dsldQeFairRidgeLin(svcensus, "wageinc", "gender", deweightPars = 
  list(occ=.4, age=.2))
lin$testAcc 
lin$corrs 
predict(lin, svcensus[1,-4])

# dsldQeFairRidgeLin: deweight "decile score" column
log <- dsldQeFairRidgeLog(compas1, "two_year_recid", "race", 
  list(decile_score=0.1), yesYVal = "Yes")
log$testAcc 
log$corrs 
predict(log, compas1[1,-8])

---

dsldFairML Wrappers

Description

Fair machine learning models: estimation and prediction. The following functions provide wrappers for some functions in the fairML package.

Usage

dsldFrrm(data, yName, sName, unfairness, definition = "sp-komiyama", 
   lambda = 0, save.auxiliary = FALSE)
dsldFgrrm(data, yName, sName, unfairness, definition = "sp-komiyama", 
   family = "binomial", lambda = 0, save.auxiliary = FALSE)
dsldNclm(data, yName, sName, unfairness, covfun = cov, lambda = 0, 
   save.auxiliary = FALSE)
dsldZlm(data, yName, sName, unfairness)
dsldZlrm(data, yName, sName, unfairness)

Arguments

data	Data frame.
yName	Name of the response variable column.
sName	Name(s) of the sensitive attribute column(s).
unfairness	A number in (0, 1]. Degree of unfairness allowed in the model. A value (very near) 0 means the model is completely fair, while a value of 1 means the model is not constrained to be fair at all.
covfun	A function computing covariance matrices.
definition	Character string, the label of the definition of fairness. Currently either 'sp-komiyama', 'eo-komiyama' or 'if-berk'.
family	A character string, either 'gaussian' to fit linear regression, 'binomial' for logistic regression, 'poisson' for log-linear regression, 'cox' for Cox proportional hazards regression, or 'multinomial' for multinomial logistic regression.
lambda	Non-negative number, a ridge-regression penalty coefficient.
save.auxiliary	A logical value, whether to save the fitted values and the residuals of the auxiliary model that constructs the debiased predictors.

Details

See documentation for the fairml package.

Value

An object of class 'dsldFairML', which includes the model information, yName, and sName.

Author(s)

S. Martha, A. Mittal, B. Ouattara, B. Zarate, J. Tran

Examples

data(svcensus) 
data(compas1)

yName <- "wageinc"
sName <- "age"
frrmOut <- dsldFrrm(svcensus, yName, sName, 0.2, definition = "sp-komiyama") 
summary(frrmOut)
predict(frrmOut, svcensus[1:10,]) 

yName <- "two_year_recid"
sName <- "age"
fgrrmOut <- dsldFgrrm(compas1, yName, sName, 0.2, definition = "sp-komiyama")  
summary(fgrrmOut)
predict(fgrrmOut, compas1[c(1:10),])

---

dsldFairUtilTrade

Description

Exploration of the Fairness-Utility Tradeoff. Finds predictive accuracy and correlation between S and predicted Y.

Usage

dsldFairUtilTrade(data,yName,sName,dsldFtnName,
   unfairness=NULL,deweightPars=NULL,yesYVal=NULL,yesSVal=NULL,
   corrType='kendall', holdout = floor(min(1000, 0.1 * nrow(data))))

Arguments

data	Data frame.
yName	Name of the response variable Y column. Y must be numeric or binary (two-level R factor).
sName	Name of the sensitive attribute S column. S must be numeric or binary (two-level R factor).
dsldFtnName	Quoted name of one of the fairML or EDF functions.
unfairness	Nonnull for the fairML functions.
deweightPars	Nonnull for the EDF functions.
yesYVal	Y value to be treated as Y = 1 for binary Y.
yesSVal	S value to be treated as S = 1 for binary S.
corrType	Either 'kendall' or 'probs'.
holdout	Size of holdout set.

Details

Tool for exploring tradeoff between utility (predictive accuracy, Mean Absolute Prediction Error or overall probability of misclassification) and fairness. Roughly speaking, the latter is defined as the strength of relation between S and predicted Y (the smaller, the better). The main issue is definition of "relation" in the case of binary Y or S:

In the 'kendall' case, binary predicted Y or S is recoded to 1s and 0s, and Kendall correlation is used. In the 'probs' case, binary Y or S is replaced by P(Y = 1 | X) and P(S = 1 | X); squared Pearson correlation is then computed.

Value

A two-component vector, consisting of predictive accuracy and strength of relation between S and predicted Y.

Author(s)

N. Matloff

Examples

data(svcensus)
dsldFairUtilTrade(svcensus,'wageinc','gender','dsldFrrm',0.2,yesSVal='male')
data(lsa)
race1 <- lsa$race1
lsabw <- lsa[race1 == 'black' | race1 == 'white',]
# need to get rid of excess levels
race1 <- lsabw$race1
race1 <- as.character(race1)
lsabw$race1 <- as.factor(race1)
dsldFairUtilTrade(lsabw,'bar','race1','dsldQeFairRidgeLog',
   deweightPars=list(fam_inc=0.1),yesYVal='TRUE',yesSVal='white')

---

dsldFreqPCoord

Description

Wrapper for the freqparcoord function from the freqparcoord package.

Usage

dsldFreqPCoord(data, m, sName = NULL, method
    = "maxdens", faceting = "vert", k = 50, klm = 5 * k, keepidxs = NULL, 
    plotidxs = FALSE, cls = NULL, plot_filename = NULL)

Arguments

data	Data frame or matrix.
m	Number of lines to plot for each group. A negative value in conjunction with the method maxdens indicates that the lowest-density lines are to be plotted. If method is locmax, then m is forced to 1.
sName	Column for the grouping variable, if any (if none, all the data is treated as a single group); the column must be a vector or factor. The column must not be in dispcols. If method is locmax, grpvar is forced to NULL
method	What to display: 'maxdens' for plotting the most (or least) typical lines, 'locmax' for cluster hunting, or 'randsamp' for plotting a random sample of lines.
faceting	How to display groups, if present. Use 'vert' for vertical stacking of group plots, 'horiz' for horizontal ones, or 'none' to draw all lines in one plot, color-coding by group.
k	Number of nearest neighbors to use for density estimation.
klm	If method is "locmax", number of nearest neighbors to use for finding local maxima for cluster hunting. Generally needs to be much larger than k, to avoid "noise fitting."
keepidxs	If not NULL, the indices of the rows of data that are plotted will be stored in a component idxs of the return value. The rows themselves will be in a component xdisp, ordered by data[,dispcols[1].
plotidxs	If TRUE, lines in the display will be annotated with their case numbers, i.e. their row numbers within data. Use only with small values of m, as overplotting may occur.
cls	Cluster, if any (see the parallel package) for parallel computation.
plot_filename	Name of the file that will hold the saved graph image. If NULL, the graph will be generated and displayed without being saved. If a filename is provided, the graph will not be displayed, only saved.

Details

The dsldFreqPCoord function wraps freqparcoord, which uses a frequency-based parallel coordinates method to vizualize multiple variables simultaneously in graph form.

This is done by plotting either the "most typical" or "least typical" (i.e. highest or lowest estimated multivariate density values respectively) cases to discern relations between variables.

The Y-axis represents the centered and scaled values of the columns.

Value

Object of type 'gg' (ggplot2 object), with components idxs and xdisp added if keepidxs is not NULL (see argument keepidxs above).

Author(s)

N. Matloff, T. Abdullah, B. Ouattara, J. Tran, B. Zarate

References

https://cran.r-project.org/web/packages/freqparcoord/index.html

Examples

data(lsa)
lsa1 <- lsa[,c('fam_inc','ugpa','gender','lsat','race1')]
dsldFreqPCoord(lsa1,75,'race1')
# a number of interesting trends among the most "typical" law students in the
# dataset: remarkably little variation among typical
# African-Americans; typical Hispanic men have low GPAs, poor LSAT
# scores there is more variation; typical Asian and Black students were
# female; Asians and Hispanics have the most variation in family income
# background

---

dsldFrequencyByS

Description

Informal assessment of C as a possible confounder in a relationship between a sensitive variable S and a variable Y.

Usage

dsldFrequencyByS(data, cName, sName)

Arguments

data	Data frame or equivalent.
cName	Name of the "C" column, an R factor.
sName	Name of the sensitive variable column, an R factor

Details

Essentially an informal assessment of the between S and C.

Consider the svcensus dataset. If for instance we are studying the effect of gender S on wage income Y, say C is occupation. If different genders have different occupation patterns, then C is a potential confounder. (Y does not explicitly appear here.)

Value

Data frame, one for each level of the sensitive variable S, and one column for each level of the confounder C. Each row sums to 1.0.

Author(s)

N. Matloff, T. Abdullah, A. Ashok, J. Tran

Examples

data(svcensus) 
dsldFrequencyByS(svcensus, cName = "educ", sName = "gender")
# not much difference in education between genders
dsldFrequencyByS(svcensus, cName = "occ", sName = "gender")
# substantial difference in occupation between genders
data(lsa)
lsa$faminc <- as.factor(lsa$fam_inc)
dsldFrequencyByS(lsa,'faminc','race1')
# distribution of family income by race

---

dsldLinear

Description

Comparison of sensitive groups via linear models, with or without interactions with the sensitive variable.

Usage

dsldLinear(data, yName, sName, interactions = FALSE, sComparisonPts = NULL, 
    useSandwich = FALSE)
## S3 method for class 'dsldLM'
summary(object,...)
## S3 method for class 'dsldLM'
predict(object,xNew,...)
## S3 method for class 'dsldLM'
coef(object,...)
## S3 method for class 'dsldLM'
vcov(object,...)

Arguments

data	Data frame.
yName	Name of the response variable Y column.
sName	Name of the sensitive attribute S column.
interactions	Logical value indicating whether or not to model interactions with the sensitive variable S.
sComparisonPts	If interactions is TRUE, a data frame of new cases for which mean Y | X will be compared across each pair of S levels. Must be in the same format as original data.
useSandwich	If TRUE, use the "sandwich" variance estimator.
object	An object returned by the dsldLinear function.
xNew	New data to be predicted. Must be in the same format as original data.
...	Further arguments.

Details

The dsldLinear function fits a linear model to the response variable Y using all other variables in data. The user may select for interactions with the sensitive variable S.

The function produces an instance of the 'dsldLM' class (an S3 object). Instances of the generic functions summary and coef are provided.

If interactions is TRUE, the function will fit m separate models, where m is the number of levels of S. Then summary will contain m+1 data frames; the first m of which will be the outputs from the individual models.

The m+1st data frame will compare the differences in conditional mean Y|X for each pair of S levels, and for each value of X in sComparisonPts. The intention is to allow users to see the comparisons of conditions for sensitive groups via linear models, with interactions with S.

The dsldDiffS function allows users to compare mean Y at that X between each pair of S level for additional new unseen data levels using the model fitted from dsldLinear.

Value

The dsldLinear function returns an S3 object of class 'dsldLM', with one component for each level of S. Each component includes information about the fitted model.

Author(s)

N. Matloff, A. Mittal, A. Ashok

Examples

data(svcensus) 

newData <- svcensus[c(1, 18), -c(4,6)] 
lin1 <- dsldLinear(svcensus, 'wageinc', 'gender', interactions = TRUE,
    newData)
coef(lin1)
vcov(lin1) 
summary(lin1)
predict(lin1, newData)

lin2 <- dsldLinear(svcensus, 'wageinc', 'gender', interactions = FALSE)
summary(lin2)

---

dsldLogit

Description

Comparison of conditions for sensitive groups via logistic regression models, with or without interactions with the sensitive variable.

Usage

dsldLogit(data, yName, sName, sComparisonPts = NULL, interactions = FALSE, 
   yesYVal)
## S3 method for class 'dsldGLM'
summary(object,...)
## S3 method for class 'dsldGLM'
predict(object,xNew,...)
## S3 method for class 'dsldGLM'
coef(object,...)
## S3 method for class 'dsldGLM'
vcov(object,...)

Arguments

data	Data frame used to train the linear model; will be split according to each level of sName in output if interactions is TRUE.
yName	Name of the response variable column.
sName	Name of the sensitive attribute column.
interactions	If TRUE, fit interactions with the sensitive variable.
sComparisonPts	If interactions is TRUE, a a data frame of new cases (minus Y,S) for which P(Y = 1| X) will be compared between each pairs of S levels. Must be in the same format as the original data.
yesYVal	Y value to be considered 'yes', to be coded 1 rather than 0.
object	An object returned by dsldLogit.
xNew	Dataframe to predict new cases. Must be in the same format as data.
...	Further arguments.

Details

The dsldLogit function fits a logistic regression model to the response variable. Interactions are handled as in dsldLinear.

Value

The dsldLog function returns an S3 object of class 'dsldGLM', with one component for each level of S. Each component includes information about the fitted model.

Author(s)

N. Matloff, A. Mittal, A. Ashok

Examples

data(lsa)
newData <- lsa[c(2,22,222,2222),-c(8,11)]
log1 <- dsldLogit(lsa,'bar','race1', newData, interactions = TRUE, 'TRUE')

coef(log1)
vcov(log1) 
summary(log1) 
predict(log1, newData)
            
log2 <- dsldLogit(data = lsa,
    yName = 'bar',sName = 'gender', 
    interactions = FALSE, yesYVal = 'TRUE')
summary(log2)

---

dsldMatchedATE

Description

Causal inference via matching models. Wrapper for Matching::Match.

Usage

dsldMatchedATE(data,yName,sName,yesSVal,yesYVal=NULL,
   propensFtn=NULL,k=NULL)

Arguments

data	Data frame.
yName	Name of the response variable column.
sName	Name of the sensitive attribute column. The attribute must be dichotomous.
yesSVal	S value to be considered "yes," to be coded 1 rather than 0.
yesYVal	Y value to be considered "yes," to be coded 1 rather than 0.
propensFtn	Either 'glm' (logistic), or 'knn'.
k	Number of nearest neighbors if propensFtn='knn'.

Details

This is a dsld wrapper for Matching::Match.

Matched analysis is typically applied to measuring "treatment effects," but is often applied in situations in which the "treatment," S here, is an immutable attribute such as race or gender. The usual issues concerning observational studies apply.

The function dsldMatchedATE finds the estimated mean difference between the matched Y pairs in the treated/nontreated (exposed and non-exposed) groups, with covariates X in data other than the yName and sName columns.

In the propensity model case, we estimate P(S = 1 | X), either by a logistic or k-NN model.

Value

Object of class 'Match'. See documentation in the Matching package.

Author(s)

N. Matloff

Examples

data(lalonde,package='Matching')
ll <- lalonde
ll$treat <- as.factor(ll$treat)
ll$re74 <- NULL
ll$re75 <- NULL
summary(dsldMatchedATE(ll,'re78','treat','1')) 
summary(dsldMatchedATE(ll,'re78','treat','1',propensFtn='glm'))
summary(dsldMatchedATE(ll,'re78','treat','1',propensFtn='knn',k=15))

---

dsldML

Description

Nonparametric comparison of sensitive groups.

Usage

dsldML(data,yName,sName,qeMLftnName,sComparisonPts="rand5",
    opts=NULL,holdout=NULL)

Arguments

data	A data frame.
yName	Name of the response variable column.
sName	Name(s) of the sensitive attribute column(s).
qeMLftnName	Quoted name of a prediction function in the qeML package.
sComparisonPts	Data frame of one or more data points at which the regression function is to be estimated for each level of S. If this is 'rand5', then the said data points will consist of five randomly chosen rows in the original dataset.
opts	An R list specifying arguments for the above qeML function.
holdout	The size of holdout set.

Details

In a linear model with no interactions, one can speak of "the" difference in mean Y given X across treatments, independent of X. In a nonparametric analysis, there is interaction by definition, and one can only speak of differences across treatments for a specific X value. Hence the need for the argument sComparisonPts.

The specified qeML function will be called on the indicated data once for each level of the sensitive variable. For each such level, estimated regression function values will be obtained for each row in sComparisonPts.

Value

An R list. The first component consists of the holdout-set prediction accuracies, while the second is a data frame predicted values for each sensitive group.

Author(s)

N. Matloff

Examples

data(svcensus) 
w <- dsldML(svcensus,'wageinc','gender',qeMLftnName='qeKNN',
   opts=list(k=50))
print(w)

---

ScatterPlot3D in dsld

Description

Plotly 3D visualization of a dataset on 3 axes, with points color-coded on a 4th variable.

Usage

dsldScatterPlot3D(data, yNames, sName, sGroups = NULL, sortedBy =
  "Name", numGroups = 8, maxPoints = NULL, xlim = NULL,
  ylim = NULL, zlim = NULL, main = NULL, colors =
  "Paired", opacity = 1, pointSize = 8)

Arguments

data	Data frame with at least 4 columns.
yNames	Vector of the indices or names of the columns of the data frame to be graphed on the 3 axes.
sName	Index or name of the column that contains the groups for which the data will be grouped by. This will affect the colors of the points of the graph. This column must be an R factor.
sGroups	Vector of the names of the groups for which the data will be grouped by. Every value in the vector must exist in the sName column of the data frame. If not supplied or is NULL, the function will create this automatically according to the sortedby and numgrps parameters. By default, the function uses the 8 alphabetically first distinct groups in the sName column.
sortedBy	Controls how sGroups is created automatically. If sGroups is supplied, this does nothing. One of three values: "Name", "Frequency", "Frequency-Descending". "Name" gets the first values alphabetically. "Frequency" gets the most frequently occuring values. "Frequency-Descending" gets the least frequently occuring values.
numGroups	Number of groups to be automatically generated by the function. If grpnames is supplied, this does nothing.
maxPoints	Limit to how many points may be displayed on the graph. There is no limit by default.
xlim, ylim, zlim	The x, y and z limits, each a vector with c(min, max).
main	The title of the graph. By default, the sName "vs. " yNames.
colors	Either a colorbrewer2.org palette name (e.g. "YlOrRd" or "Blues"), or a vector of colors to interpolate in hexadecimal "#RRGGBB" format, or a color interpolation function like colorRamp().
opacity	A value between 0 and 1.
pointSize	A value above 1.

Details

An interactive Plotly visualization will be created, with the three variables specified in yNames. Points will be color-coded according to sName. The plot can be rotated etc. using the mouse.

Value

No value, plot.

Author(s)

J. Tran and B. Zarate

References

https://plotly.com/r/3d-scatter-plots/

Examples

data(lsa)
dsldScatterPlot3D(lsa,sName = "race1", 
   yNames=c("ugpa", "lsat","age"), xlim=c(2,4))

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dsldTakeALookAround

Description

Evaluate feature sets for predicting Y while considering the Fairness-Utility Tradeoff.

Usage

dsldTakeALookAround(data, yName, sName, maxFeatureSetSize = (ncol(data) - 2), 
    holdout = floor(min(1000,0.1*nrow(data))))

Arguments

data	Data frame.
yName	Name of the response variable column.
sName	Name of the sensitive attribute column.
maxFeatureSetSize	Maximum number of combinations of features to be included in the data frame.
holdout	If not NULL, form a holdout set of the specified size. After fitting to the remaining data, evaluate accuracy on the test set.

Details

This function provides a tool for exploring feature combinations to use in predicting an outcome Y from features X and a sensitive variable S.

The features in X will first be considered singly, then doubly and so on, up though feature combination size maxFeatureSetSize. Y is prediction from X either a linear model (numeric Y) or logit (dichotomous Y).

The accuracy (based on qeML holdout) will be computed for each of these cases: (a) Y predicted from the given feature combination C, (b) Y predicted from the given feature combination C plus S, and (c) S predicted from C. The difference between columns 'a' and 'b' shows the sacrifice in utility stemming from not using S in our prediction of Y. (Due to sampling variation, it is possible for column 'b' to be larger than 'a'.) The value in column 'c' shows fairness, the smaller the fairer.

Value

Data frame whose first column consists of the variable names, followed by columns 'a', 'b' and 'c' as described in 'details'.

Author(s)

N. Matloff, A. Ashok, S. Martha, A. Mittal

Examples

# investigate predictive accuracy for a continuous Y,
# 'wageinc', using the default arguments for maxFeatureSetSize = 4
data(svcensus)
dsldTakeALookAround(svcensus, 'wageinc', 'gender', 4)

# investigate the predictive accuracy for a categorical Y, 
# 'educ', using the default arguments for maxFeatureSetSize = 4
dsldTakeALookAround(svcensus, 'educ', 'gender')

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Labor Market Discrimination

Description

Fictional CVs sent to real employers to investigate discrimination via given names. See Mullainathan and Bertran (2004).

References

• Mullainathan, S. and Bertran, M. (2004). Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination. American Economic Review, 94:991-1013

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Mortgage Denial

Description

The dataset provides applicant information (including race, income, loan information, etc.) The response variable indicates whether or not the applicant was approved for the loan. Additional details can be found in the SortedEffects package.

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Silicon Valley programmers and engineers data

Description

Via qeML: This data set is adapted from the 2000 Census, restricted to programmers and engineers in the Silicon Valley area.

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Utitlities

Description

Attempts to load the specified package, halting execution upon failure.

Usage

getSuggestedLib(pkgName)

Arguments

pkgName

Name of the package to be checked/loaded.

Value

No value, just side effects.

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