# summary.ATE: Summarizing output of study. In ATE: Inference for Average Treatment Effects using Covariate Balancing

## Description

`summary` method for class `"ATE"`

## Usage

 ```1 2 3 4 5``` ```## S3 method for class 'ATE' summary(object, ...) ## S3 method for class 'summary.ATE' print(x, ...) ```

## Arguments

 `object` An object of class `"ATE"`, usually a result of a call to `ATE`. `x` An object of class `"summary.ATE"`, usually a result of a call to `summary.ATE`. `...` Further arguments passed to or from methods.

## Details

`print.summary.ATE` prints a simplified output similar to `print.summary.lm`. The resulting table provides the point estimates, estimated standard errors, 95% Wald confidence intervals, the Z-statistic and the P-values for a Z-test.

## Value

The function `summary.ATE` returns a list with the following components

 `Estimate` A matrix with point estimates along with standard errors, confidence intervals etc. This is the matrix users see with the `print.summary.RIPW` function. `vcov` The variance-covariance matrix of the point estimates. `Conv` The convergence result of the `object`. `weights` The weights for each subject in each treatment arm. These are same as the `weight` component of the `"RIPW" object`. `call` The `call` passed on as an argument of the function which is equivalent to `object\$call`.

## 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``` ```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) #plot(fit1) #estimation of average treatment effects on treated (ATT) fit2<-ATE(Y,treat,X,ATT=TRUE) summary(fit2) #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) #plot(fit3) ```