R: Perform conditional decomposition with nuisance functions...

cdgd1_manual {cdgd}

R Documentation

Perform conditional decomposition with nuisance functions estimated beforehand

Description

This function gives user full control over the estimation of the nuisance functions. For the unconditional decomposition, ten nuisance functions need to be estimated. The nuisance functions should be estimated using cross-fitting if Donsker class is not assumed.

Usage

cdgd1_manual(
  Y,
  D,
  G,
  YgivenGXQ.Pred_D0,
  YgivenGXQ.Pred_D1,
  DgivenGXQ.Pred,
  Y0givenQ.Pred_G0,
  Y0givenQ.Pred_G1,
  Y1givenQ.Pred_G0,
  Y1givenQ.Pred_G1,
  DgivenQ.Pred_G0,
  DgivenQ.Pred_G1,
  GgivenQ.Pred,
  data,
  alpha = 0.05
)

Arguments

`Y`	Outcome. The name of a numeric variable.
`D`	Treatment status. The name of a binary numeric variable taking values of 0 and 1.
`G`	Advantaged group membership. The name of a binary numeric variable taking values of 0 and 1.
`YgivenGXQ.Pred_D0`	A numeric vector of predicted Y values given X, G, and D=0. Vector length=nrow(data).
`YgivenGXQ.Pred_D1`	A numeric vector of predicted Y values given X, G, and D=1. Vector length=nrow(data).
`DgivenGXQ.Pred`	A numeric vector of predicted D values given X and G. Vector length=nrow(data).
`Y0givenQ.Pred_G0`	A numeric vector of predicted Y(0) values given Q and G=0. Vector length=nrow(data).
`Y0givenQ.Pred_G1`	A numeric vector of predicted Y(0) values given Q and G=0. Vector length=nrow(data).
`Y1givenQ.Pred_G0`	A numeric vector of predicted Y(1) values given Q and G=0. Vector length=nrow(data).
`Y1givenQ.Pred_G1`	A numeric vector of predicted Y(1) values given Q and G=1. Vector length=nrow(data).
`DgivenQ.Pred_G0`	A numeric vector of predicted D values given Q and G=0. Vector length=nrow(data).
`DgivenQ.Pred_G1`	A numeric vector of predicted D values given Q and G=0. Vector length=nrow(data).
`GgivenQ.Pred`	A numeric vector of predicted G values given Q. Vector length=nrow(data).
`data`	A data frame.
`alpha`	1-alpha confidence interval.

Value

A dataframe of estimates.

Examples

# This example will take a minute to run.

data(exp_data)

Y="outcome"
D="treatment"
G="group_a"
X="confounder"
Q="Q"
data=exp_data

set.seed(1)

### estimate the nuisance functions with cross-fitting
sample1 <- sample(nrow(data), floor(nrow(data)/2), replace=FALSE)
sample2 <- setdiff(1:nrow(data), sample1)

### outcome regression model

message <- utils::capture.output( YgivenDGXQ.Model.sample1 <-
  caret::train(stats::as.formula(paste(Y, paste(D,G,Q,paste(X,collapse="+"),sep="+"), sep="~")),
               data=data[sample1,], method="ranger",
               trControl=caret::trainControl(method="cv"),
               tuneGrid=expand.grid(mtry=c(2,4),splitrule=c("variance"),min.node.size=c(50,100))) )
message <- utils::capture.output( YgivenDGXQ.Model.sample2 <-
  caret::train(stats::as.formula(paste(Y, paste(D,G,Q,paste(X,collapse="+"),sep="+"), sep="~")),
               data=data[sample2,], method="ranger",
               trControl=caret::trainControl(method="cv"),
               tuneGrid=expand.grid(mtry=c(2,4),splitrule=c("variance"),min.node.size=c(50,100))) )

### propensity score model
data[,D] <- as.factor(data[,D])
levels(data[,D]) <- c("D0","D1")  # necessary for caret implementation of ranger

message <- utils::capture.output( DgivenGXQ.Model.sample1 <-
    caret::train(stats::as.formula(paste(D, paste(G,Q,paste(X,collapse="+"),sep="+"), sep="~")),
                 data=data[sample1,], method="ranger",
                 trControl=caret::trainControl(method="cv", classProbs=TRUE),
                 tuneGrid=expand.grid(mtry=c(1,2),splitrule=c("gini"),min.node.size=c(50,100))) )
message <- utils::capture.output( DgivenGXQ.Model.sample2 <-
    caret::train(stats::as.formula(paste(D, paste(G,Q,paste(X,collapse="+"),sep="+"), sep="~")),
                 data=data[sample2,], method="ranger",
                 trControl=caret::trainControl(method="cv", classProbs=TRUE),
                 tuneGrid=expand.grid(mtry=c(1,2),splitrule=c("gini"),min.node.size=c(50,100))) )

data[,D] <- as.numeric(data[,D])-1

### cross-fitted predictions
YgivenGXQ.Pred_D0 <- YgivenGXQ.Pred_D1 <- DgivenGXQ.Pred <- rep(NA, nrow(data))

pred_data <- data
pred_data[,D] <- 0
YgivenGXQ.Pred_D0[sample2] <- stats::predict(YgivenDGXQ.Model.sample1,
    newdata = pred_data[sample2,])
YgivenGXQ.Pred_D0[sample1] <- stats::predict(YgivenDGXQ.Model.sample2,
    newdata = pred_data[sample1,])

pred_data <- data
pred_data[,D] <- 1
YgivenGXQ.Pred_D1[sample2] <- stats::predict(YgivenDGXQ.Model.sample1,
    newdata = pred_data[sample2,])
YgivenGXQ.Pred_D1[sample1] <- stats::predict(YgivenDGXQ.Model.sample2,
    newdata = pred_data[sample1,])

pred_data <- data
DgivenGXQ.Pred[sample2] <- stats::predict(DgivenGXQ.Model.sample1,
    newdata = pred_data[sample2,], type="prob")[,2]
DgivenGXQ.Pred[sample1] <- stats::predict(DgivenGXQ.Model.sample2,
    newdata = pred_data[sample1,], type="prob")[,2]

### Estimate E(Y_d | Q,g)
YgivenGXQ.Pred_D1_ncf <- YgivenGXQ.Pred_D0_ncf <- DgivenGXQ.Pred_ncf <- rep(NA, nrow(data))
# ncf stands for non-cross-fitted

pred_data <- data
pred_data[,D] <- 1
YgivenGXQ.Pred_D1_ncf[sample1] <- stats::predict(YgivenDGXQ.Model.sample1,
    newdata = pred_data[sample1,])
YgivenGXQ.Pred_D1_ncf[sample2] <- stats::predict(YgivenDGXQ.Model.sample2,
    newdata = pred_data[sample2,])

pred_data <- data
pred_data[,D] <- 0
YgivenGXQ.Pred_D0_ncf[sample1] <- stats::predict(YgivenDGXQ.Model.sample1,
    newdata = pred_data[sample1,])
YgivenGXQ.Pred_D0_ncf[sample2] <- stats::predict(YgivenDGXQ.Model.sample2,
    newdata = pred_data[sample2,])

DgivenGXQ.Pred_ncf[sample1] <- stats::predict(DgivenGXQ.Model.sample1,
    newdata = pred_data[sample1,], type="prob")[,2]
DgivenGXQ.Pred_ncf[sample2] <- stats::predict(DgivenGXQ.Model.sample2,
    newdata = pred_data[sample2,], type="prob")[,2]

# IPOs for modelling E(Y_d | Q,g)
IPO_D0_ncf <- (1-data[,D])/(1-DgivenGXQ.Pred_ncf)/mean((1-data[,D])/(1-DgivenGXQ.Pred_ncf))*
    (data[,Y]-YgivenGXQ.Pred_D0_ncf) + YgivenGXQ.Pred_D0_ncf
IPO_D1_ncf <- data[,D]/DgivenGXQ.Pred_ncf/mean(data[,D]/DgivenGXQ.Pred_ncf)*
    (data[,Y]-YgivenGXQ.Pred_D1_ncf) + YgivenGXQ.Pred_D1_ncf

data_temp <- data[,c(G,Q)]
data_temp$IPO_D0_ncf <- IPO_D0_ncf
data_temp$IPO_D1_ncf <- IPO_D1_ncf

message <- utils::capture.output( Y0givenGQ.Model.sample1 <-
    caret::train(stats::as.formula(paste("IPO_D0_ncf", paste(G,Q,sep="+"), sep="~")),
                 data=data_temp[sample1,], method="ranger",
                 trControl=caret::trainControl(method="cv"),
                 tuneGrid=expand.grid(mtry=1,splitrule=c("variance"),min.node.size=c(25,50))) )
message <- utils::capture.output( Y0givenGQ.Model.sample2 <-
    caret::train(stats::as.formula(paste("IPO_D0_ncf", paste(G,Q,sep="+"), sep="~")),
                 data=data_temp[sample2,], method="ranger",
                 trControl=caret::trainControl(method="cv"),
                 tuneGrid=expand.grid(mtry=1,splitrule=c("variance"),min.node.size=c(25,50))) )
message <- utils::capture.output( Y1givenGQ.Model.sample1 <-
    caret::train(stats::as.formula(paste("IPO_D1_ncf", paste(G,Q,sep="+"), sep="~")),
                 data=data_temp[sample1,], method="ranger",
                 trControl=caret::trainControl(method="cv"),
                 tuneGrid=expand.grid(mtry=1,splitrule=c("variance"),min.node.size=c(25,50))) )
message <- utils::capture.output( Y1givenGQ.Model.sample2 <-
    caret::train(stats::as.formula(paste("IPO_D1_ncf", paste(G,Q,sep="+"), sep="~")),
                 data=data_temp[sample2,], method="ranger",
                 trControl=caret::trainControl(method="cv"),
                 tuneGrid=expand.grid(mtry=1,splitrule=c("variance"),min.node.size=c(25,50))) )

Y0givenQ.Pred_G0 <- Y0givenQ.Pred_G1 <- Y1givenQ.Pred_G0 <- Y1givenQ.Pred_G1 <- rep(NA, nrow(data))

pred_data <- data
pred_data[,G] <- 1
# cross-fitting is used
Y0givenQ.Pred_G1[sample2] <- stats::predict(Y0givenGQ.Model.sample1, newdata = pred_data[sample2,])
Y0givenQ.Pred_G1[sample1] <- stats::predict(Y0givenGQ.Model.sample2, newdata = pred_data[sample1,])
Y1givenQ.Pred_G1[sample2] <- stats::predict(Y1givenGQ.Model.sample1, newdata = pred_data[sample2,])
Y1givenQ.Pred_G1[sample1] <- stats::predict(Y1givenGQ.Model.sample2, newdata = pred_data[sample1,])

pred_data <- data
pred_data[,G] <- 0
# cross-fitting is used
Y0givenQ.Pred_G0[sample2] <- stats::predict(Y0givenGQ.Model.sample1, newdata = pred_data[sample2,])
Y0givenQ.Pred_G0[sample1] <- stats::predict(Y0givenGQ.Model.sample2, newdata = pred_data[sample1,])
Y1givenQ.Pred_G0[sample2] <- stats::predict(Y1givenGQ.Model.sample1, newdata = pred_data[sample2,])
Y1givenQ.Pred_G0[sample1] <- stats::predict(Y1givenGQ.Model.sample2, newdata = pred_data[sample1,])

### Estimate E(D | Q,g')
data[,D] <- as.factor(data[,D])
levels(data[,D]) <- c("D0","D1")  # necessary for caret implementation of ranger

message <- utils::capture.output( DgivenGQ.Model.sample1 <-
    caret::train(stats::as.formula(paste(D, paste(G,Q,sep="+"), sep="~")),
                 data=data[sample1,], method="ranger",
                 trControl=caret::trainControl(method="cv", classProbs=TRUE),
                 tuneGrid=expand.grid(mtry=1,splitrule=c("gini"),min.node.size=c(25,50))) )
message <- utils::capture.output( DgivenGQ.Model.sample2 <-
    caret::train(stats::as.formula(paste(D, paste(G,Q,sep="+"), sep="~")),
                 data=data[sample2,], method="ranger",
                 trControl=caret::trainControl(method="cv", classProbs=TRUE),
                 tuneGrid=expand.grid(mtry=1,splitrule=c("gini"),min.node.size=c(25,50))) )

data[,D] <- as.numeric(data[,D])-1

DgivenQ.Pred_G0 <- DgivenQ.Pred_G1 <- rep(NA, nrow(data))

pred_data <- data
pred_data[,G] <- 0
DgivenQ.Pred_G0[sample2] <- stats::predict(DgivenGQ.Model.sample1,
    newdata = pred_data[sample2,], type="prob")[,2]
DgivenQ.Pred_G0[sample1] <- stats::predict(DgivenGQ.Model.sample2,
    newdata = pred_data[sample1,], type="prob")[,2]

pred_data <- data
pred_data[,G] <- 1
DgivenQ.Pred_G1[sample2] <- stats::predict(DgivenGQ.Model.sample1,
   newdata = pred_data[sample2,], type="prob")[,2]
DgivenQ.Pred_G1[sample1] <- stats::predict(DgivenGQ.Model.sample2,
   newdata = pred_data[sample1,], type="prob")[,2]

### Estimate p_g(Q)=Pr(G=g | Q)
data[,G] <- as.factor(data[,G])
levels(data[,G]) <- c("G0","G1")  # necessary for caret implementation of ranger

message <- utils::capture.output( GgivenQ.Model.sample1 <-
    caret::train(stats::as.formula(paste(G, paste(Q,sep="+"), sep="~")),
                 data=data[sample1,], method="ranger",
                 trControl=caret::trainControl(method="cv", classProbs=TRUE),
                 tuneGrid=expand.grid(mtry=1,splitrule=c("gini"),min.node.size=c(25,50))) )
message <- utils::capture.output( GgivenQ.Model.sample2 <-
    caret::train(stats::as.formula(paste(G, paste(Q,sep="+"), sep="~")),
                 data=data[sample2,], method="ranger",
                 trControl=caret::trainControl(method="cv", classProbs=TRUE),
                 tuneGrid=expand.grid(mtry=1,splitrule=c("gini"),min.node.size=c(25,50))) )

data[,G] <- as.numeric(data[,G])-1

GgivenQ.Pred <- rep(NA, nrow(data))
GgivenQ.Pred[sample2] <- stats::predict(GgivenQ.Model.sample1,
    newdata = data[sample2,], type="prob")[,2]
GgivenQ.Pred[sample1] <- stats::predict(GgivenQ.Model.sample2,
    newdata = data[sample1,], type="prob")[,2]


results <- cdgd1_manual(Y=Y,D=D,G=G,
                        YgivenGXQ.Pred_D0=YgivenGXQ.Pred_D0,
                        YgivenGXQ.Pred_D1=YgivenGXQ.Pred_D1,
                        DgivenGXQ.Pred=DgivenGXQ.Pred,
                        Y0givenQ.Pred_G0=Y0givenQ.Pred_G0,
                        Y0givenQ.Pred_G1=Y0givenQ.Pred_G1,
                        Y1givenQ.Pred_G0=Y1givenQ.Pred_G0,
                        Y1givenQ.Pred_G1=Y1givenQ.Pred_G1,
                        DgivenQ.Pred_G0=DgivenQ.Pred_G0,
                        DgivenQ.Pred_G1=DgivenQ.Pred_G1,
                        GgivenQ.Pred=GgivenQ.Pred,
                        data,alpha=0.05)

results

[Package cdgd version 0.3.5 Index]