# plot.ATE: Plots of empirical and weighted CDF for covariates In ATE: Inference for Average Treatment Effects using Covariate Balancing

## Description

Plot function for class "ATE"

## Usage

 ```1 2``` ```## S3 method for class 'ATE' plot(x, ...) ```

## Arguments

 `x` An object of type `"ATE"`. `...` Further arguments passed to or from the function.

## Details

This function plots the empirical CDF and weighted empirical CDF for each covariate to demonstrate the effect of covariate balancing and for graphical diagnostics. In observational studies with confounding, the covariate distributions are different for each treatment arms. Comparisons of unweighted empirical CDF would demonstrate this difference. The balancing weights constructed by balancing moments of covariate distributions, and the weighted CDF would show an improved balance.

## Author(s)

`ATE`
 ``` 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 43 44 45 46 47``` ```library(ATE) #binary treatment set.seed(25) n <- 200 Z <- matrix(rnorm(4*n),ncol=4,nrow=n) prop <- 1 / (1 + exp(Z[,1] - 0.5 * Z[,2] + 0.25*Z[,3] + 0.1 * Z[,4])) treat <- rbinom(n, 1, prop) Y <- 200 + 10*treat+ (1.5*treat-0.5)*(27.4*Z[,1] + 13.7*Z[,2] + 13.7*Z[,3] + 13.7*Z[,4]) + rnorm(n) X <- cbind(exp(Z[,1])/2,Z[,2]/(1+exp(Z[,1])), (Z[,1]*Z[,3]/25+0.6)^3,(Z[,2]+Z[,4]+20)^2) #estimation of average treatment effects (ATE) fit1<-ATE(Y,treat,X) summary(fit1) ####################UNCOMMENT THE NEXT LINE###################### #plot(fit1) #estimation of average treatment effects on treated (ATT) fit2<-ATE(Y,treat,X,ATT=TRUE) summary(fit2) ####################UNCOMMENT THE NEXT LINE###################### #plot(fit2) #three treatment groups set.seed(25) n <- 200 Z <- matrix(rnorm(4*n),ncol=4,nrow=n) prop1 <- 1 / (1 + exp(1+Z[,1] - 0.5 * Z[,2] + 0.25*Z[,3] + 0.1 * Z[,4])) prop2 <- 1 / (1 + exp(Z[,1] - 0.5 * Z[,2] + 0.25*Z[,3] + 0.1 * Z[,4])) U <-runif(n) treat <- numeric(n) treat[U>(1-prop2)]=2 treat[U<(1-prop2)& U>(prop2-prop1)]=1 Y <- 210 + 10*treat +(27.4*Z[,1] + 13.7*Z[,2] + 13.7*Z[,3] + 13.7*Z[,4]) + rnorm(n) X <- cbind(exp(Z[,1])/2,Z[,2]/(1+exp(Z[,1])), (Z[,1]*Z[,3]/25+0.6)^3,(Z[,2]+Z[,4]+20)^2) fit3<-ATE(Y,treat,X) summary(fit3) ####################UNCOMMENT THE NEXT LINE###################### #plot(fit3) ```