# Perform unconditional decomposition with nuisance functions estimated beforehand This function gives the user full control over the estimation of the nuisance functions. For the unconditional decomposition, three nuisance functions (YgivenGX.Pred_D0, YgivenGX.Pred_D1, and DgivenGX.Pred) need to be estimated. The nuisance functions should be estimated using cross-fitting if Donsker class is not assumed. ```r cdgd0_manual( Y, D, G, YgivenGX.Pred_D1, YgivenGX.Pred_D0, DgivenGX.Pred, data, alpha = 0.05, weight = NULL ) ``` ## 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. - `YgivenGX.Pred_D1`: A numeric vector of predicted Y values given X, G, and D=1. Vector length=nrow(data). - `YgivenGX.Pred_D0`: A numeric vector of predicted Y values given X, G, and D=0. Vector length=nrow(data). - `DgivenGX.Pred`: A numeric vector of predicted D values given X and G. Vector length=nrow(data). - `data`: A data frame. - `alpha`: 1-alpha confidence interval. - `weight`: Sampling weights. The name of a numeric variable. If unspecified, equal weights are used. Technically, the weight should be a deterministic function of X and G. ## Returns A list of estimates. ## Examples ```r # This example will take a minute to run. data(exp_data) Y="outcome" D="treatment" G="group_a" X=c("Q","confounder") 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( YgivenDGX.Model.sample1 <- caret::train(stats::as.formula(paste(Y, paste(D,G,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( YgivenDGX.Model.sample2 <- caret::train(stats::as.formula(paste(Y, paste(D,G,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( DgivenGX.Model.sample1 <- caret::train(stats::as.formula(paste(D, paste(G,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( DgivenGX.Model.sample2 <- caret::train(stats::as.formula(paste(D, paste(G,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 YgivenGX.Pred_D0 <- YgivenGX.Pred_D1 <- DgivenGX.Pred <- rep(NA, nrow(data)) pred_data <- data pred_data[,D] <- 0 YgivenGX.Pred_D0[sample2] <- stats::predict(YgivenDGX.Model.sample1, newdata = pred_data[sample2,]) YgivenGX.Pred_D0[sample1] <- stats::predict(YgivenDGX.Model.sample2, newdata = pred_data[sample1,]) pred_data <- data pred_data[,D] <- 1 YgivenGX.Pred_D1[sample2] <- stats::predict(YgivenDGX.Model.sample1, newdata = pred_data[sample2,]) YgivenGX.Pred_D1[sample1] <- stats::predict(YgivenDGX.Model.sample2, newdata = pred_data[sample1,]) pred_data <- data DgivenGX.Pred[sample2] <- stats::predict(DgivenGX.Model.sample1, newdata = pred_data[sample2,], type="prob")[,2] DgivenGX.Pred[sample1] <- stats::predict(DgivenGX.Model.sample2, newdata = pred_data[sample1,], type="prob")[,2] results <- cdgd0_manual(Y=Y,D=D,G=G, YgivenGX.Pred_D0=YgivenGX.Pred_D0, YgivenGX.Pred_D1=YgivenGX.Pred_D1, DgivenGX.Pred=DgivenGX.Pred, data=data) results ```

cdgd0_manual function