Description Usage Arguments Details Value Examples
conformalIte
supports four algorithms: the nested approach with exact and inexact
calibration for cases with both potential outcomes missing, the naive approach for cases with both potential outcomes missing and the counterfactual
inference for cases with only one potential outcome missing. For each algorithm, it supports both
split conformal inference and CV+, including weighted Jackknife+ as a special case. For each type, it
supports both conformalized quantile regression (CQR) and standard conformal inference based on conditional mean regression.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28  conformalIte(
X,
Y,
T,
alpha = 0.1,
algo = c("nest", "naive", "counterfactual"),
exact = FALSE,
type = c("CQR", "mean"),
side = c("two", "above", "below"),
quantiles = NULL,
outfun = NULL,
outparams = list(),
psfun = NULL,
psparams = list(),
cfprop = 0.5,
citype = c("CQR", "mean"),
lofun = NULL,
loquantile = 0.4,
loparams = list(),
upfun = NULL,
upquantile = 0.6,
upparams = list(),
useCV = FALSE,
trainprop = 0.75,
nfolds = 10,
wthigh = 20,
wtlow = 0.05
)

X 
covariates. 
Y 
observed outcome vector. 
T 
treatment indicator, a binary vector. 
alpha 
confidence level. 
algo 
a string that takes values in {"nest", "naive", "counterfactual"}. See Details. 
exact 
a logical indicating whether the exact calibration is used for nested approach. Used only when 
type 
a string that takes values in {"CQR", "mean"}. 
side 
a string that takes values in {"two", "above", "below"}. See Details. 
quantiles 
for covariates in the training data. Used only when 
outfun 
a function that models the conditional mean or quantiles, or a valid string.
The default is random forest when 
outparams 
a list of other parameters to be passed into 
psfun 
a function that models the missing mechanism (probability of missing given X), or a valid string. The default is "Boosting". See Details. 
psparams 
a list of other parameters to be passed into 
cfprop 
the proportion of units to be used to compute ITE intervals in nested approach. Used only when

citype 
the type of interval conformal inference used in the nested approach with exact calibration.
Used only when 
lofun 
the function to fit the lower bound, or a valid string. Used only when

loquantile 
the quantile to fit for 
loparams 
a list of other parameters to be passed into 
upfun 
the function to fit the upper bound, or a valid string. Used only when

upquantile 
the quantile to fit for 
upparams 
a list of other parameters to be passed into 
useCV 
FALSE for split conformal inference and TRUE for CV+. 
trainprop 
proportion of units for training 
nfolds 
number of folds. The default is 10. Used only when 
wthigh 
upper truncation level of weights. See 
wtlow 
lower truncation level of weights. See 
The algorithm to be used is controlled by algo
and exact
:
(Default) when algo = "nest"
and exact = FALSE
, the inexact nested approach is used. It
first splits the data into two folds, with the second fold including cfprop
fraction of units. Then it applies
conformalCf
on the first fold to compute counterfactual intervals on the second fold, which further yields
interval estimates of ITE \hat{C}(X_i). Finally it fits \hat{C}^{L}(X_i) and \hat{C}^{R}(X_i) on X_i's.
When algo = "nest"
and exact = TRUE
, the exact nested approach is used. It has the same steps as the inexact nested approach to produce
ITE intervals \hat{C}(X_i)'s on the second fold but then applies conformalInt
to calibrate them.
When algo = "naive"
, the naive approach is used. It applies conformalCf
on the data and
produce counterfactual intervals for both Y(1) and Y(0). The ITE intervals are computed by contrasting two counterfactual intervals.
When algo = "counterfactual"
, it handles the case where the treatment assignments and the observed outcome are
both available for each testing point. As with the naive approach, it applies conformalCf
on the data and
produce counterfactual intervals for both Y(1) and Y(0). The ITE intervals are then computed by contrasting the observed outcome
and the interval for the missing potential outcome.
When side = "above"
,
intervals are of form [Inf, a(x)] and when side = "below"
the intervals are of form [a(x), Inf].
When type = "CQR"
, quantiles
must be a vector of 2, regardless of side
. When side = "two"
, quantiles
will be used in outfun
for both Y(1) and Y(0); when side = "above"
or "below"
, quantiles[1]
will be used for Y(0) and quantiles[2]
will be used for Y(1).
outfun
is applied to both Y(1) and Y(0). outfun
can be a valid string, including
"RF" for random forest that predicts the conditional mean, a wrapper built on randomForest
package.
Used when type = "mean"
.
"quantRF" for quantile random forest that predicts the conditional quantiles, a wrapper built on
grf
package. Used when type = "CQR"
.
"Boosting" for gradient boosting that predicts the conditional mean, a wrapper built on gbm
package. Used when type = "mean"
.
"quantBoosting" for quantile gradient boosting that predicts the conditional quantiles, a wrapper built on
gbm
package. Used when type = "CQR"
.
"BART" for gradient boosting that predicts the conditional mean, a wrapper built on bartMachine
package. Used when type = "mean"
.
"quantBART" for quantile gradient boosting that predicts the conditional quantiles, a wrapper built on
bartMachine
package. Used when type = "CQR"
.
or a function object whose input must include, but not limited to
Y
for outcome in the training data.
X
for covariates in the training data.
Xtest
for covariates in the testing data.
When type = "CQR"
, outfun
should also include an argument quantiles
that is either
a vector of length 2 or a scalar, depending on the argument side
. The output of outfun
must be a matrix with two columns giving the conditional quantile estimates when quantiles
is
a vector of length 2; otherwise, it must be a vector giving the conditional quantile estimate or
conditional mean estimate. Other optional arguments can be passed into outfun
through outparams
.
lofun
and upfun
have the same forms as outfun
except that the input quantiles
must be scalar when citype = "CQR"
, instead of a vector of 2, because only one conditional quantile
is fitted. The argument loquantile
is used for lofun
and the argument hiquantile
is used
for upfun
. Moreover, the output must be a vector giving the conditional quantile estimate or conditional mean
estimate. Other optional arguments can be passed into lofun
through loparams
and upfun
through upparams
.
psfun
can be a valid string, including
"RF" for random forest that predicts the propensity score, a wrapper built on randomForest
package.
Used when type = "mean"
.
"Boosting" for gradient boosting that predicts the propensity score, a wrapper built on gbm
package. Used when type = "mean"
.
or a function object whose input must include, but not limited to
Y
for treatment assignment, a binary vector, in the training data.
X
for covariates in the training data.
Xtest
for covariates in the testing data.
The output of psfun
must be a vector of predicted probabilities. Other optional arguments
can be passed into psfun
through psparams
.
a function that outputs the interval estimates on a given dataset. When algo = "nest"
or "naive"
, it takes
a single input X
; when algo = "counterfactual"
, it takes three inputs X
, Y
and T
.
#' @seealso
conformal
, conformalInt
, conformalCf
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42  # Generate potential outcomes from two linear models
set.seed(1)
n < 1000
d < 5
X < matrix(rnorm(n * d), nrow = n)
beta < rep(1, 5)
Y1 < X %*% beta + rnorm(n)
Y0 < rnorm(n)
# Generate treatment indicators
ps < pnorm(X[, 1])
T < as.numeric(ps < runif(n))
Y < ifelse(T == 1, Y1, Y0)
# Generate testing data
ntest < 5
Xtest < matrix(rnorm(ntest * d), nrow = ntest)
# Inexact nested method
CIfun < conformalIte(X, Y, T, alpha = 0.1, algo = "nest", exact = FALSE, type = "CQR",
quantiles = c(0.05, 0.95), outfun = "quantRF", useCV = FALSE)
CIfun(Xtest)
# Exact nested method
CIfun < conformalIte(X, Y, T, alpha = 0.1, algo = "nest", exact = TRUE, type = "CQR",
quantiles = c(0.05, 0.95), outfun = "quantRF", useCV = FALSE)
CIfun(Xtest)
# naive method
CIfun < conformalIte(X, Y, T, alpha = 0.1, algo = "naive", type = "CQR",
quantiles = c(0.05, 0.95), outfun = "quantRF", useCV = FALSE)
CIfun(Xtest)
# counterfactual method, Y and T needs to be observed
pstest < pnorm(Xtest[, 1])
Ttest < as.numeric(pstest < runif(ntest))
Y1test < Xtest %*% beta + rnorm(ntest)
Y0test < rnorm(ntest)
Ytest < ifelse(Ttest == 1, Y1test, Y0test)
CIfun < conformalIte(X, Y, T, alpha = 0.1, algo = "counterfactual", type = "CQR",
quantiles = c(0.05, 0.95), outfun = "quantRF", useCV = FALSE)
CIfun(Xtest, Ytest, Ttest)

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