The main aim of ATE is to provide a user-friendly interface for
nonparametric efficient inference of average treatment effects for
observational data. The package provides point estimates for average
treatment effects, average treatment effect on the treated and can also
handle the case of multiple treatments. The package also allows
inference by consistent variance estimates.
R (>=3.2.0)Rcpp (>=0.12.0)RcppArmadilloMatrix
The package can be installed from CRAN:
install.packages("ATE")Alternatively, we can directly install from Github using the devtools
package:
devtools::install_github("asadharis/ATE")- Ease of use: The main function
ATErequires only a numeric matrixXof covariates, numeric vectorYof response andtreatvector indicating treatment assignment.
set.seed(1)
library(ATE)
#Generate some data
n <- 500
X1 <- matrix(rnorm(n * 5), ncol = 5)
X2 <- matrix(rbinom(3 * n, 1, 0.4), ncol = 3)
X <- cbind(X1, X2)
prop <- 1 / (1 + exp(X[, 1] - 0.5 * X[, 2] + 0.25 * X[, 3] + X[, 6] + 0.5 * X[, 8]))
treat <- rbinom(n, 1, prop)
Y <- 10 * treat + (2 * treat - 1) *
(X[, 1] - 0.5 * X[, 2] + 0.25 * X[, 3] + X[, 6] + 0.5 * X[, 8]) + rnorm(n)
#Fit ATE object
fit1 <- ATE(Y, treat, X)
summary(fit1)#> Call:
#> ATE(Y = Y, treat = treat, X = X)
#>
#> Estimate Std. Error 95%.Lower 95%.Upper z value p value
#> E[Y(1)] 10.650818 0.112995 10.429353 10.872284 94.2594 < 2.2e-16 ***
#> E[Y(0)] -0.708631 0.088772 -0.882621 -0.534641 -7.9826 1.433e-15 ***
#> ATE 11.359449 0.169154 11.027913 11.690986 67.1544 < 2.2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plotfunction for demonstrating effect of covariate balancing for continuous and binary covariates.
plot(fit1)
- We can also estimate the average treatment effect on the treated.
fit2 <- ATE(Y, treat, X, ATT = TRUE)
summary(fit2)#> Call:
#> ATE(Y = Y, treat = treat, X = X, ATT = TRUE)
#>
#> Estimate Std. Error 95%.Lower 95%.Upper z value p value
#> E[Y(1)|T=1] 9.820802 0.114407 9.596569 10.045035 85.8412 <2e-16 ***
#> E[Y(0)|T=1] 0.158785 0.127597 -0.091301 0.408870 1.2444 0.2133
#> ATT 9.662018 0.214933 9.240757 10.083278 44.9537 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ATEautomatically detects and estimates the case of multiple treatment arms.
treat <- rbinom(n, 3, prop)
Y <- 10 * treat + (2 * treat - 1) *
(X[, 1] - 0.5 * X[, 2] + 0.25 * X[, 3] + X[, 6] + 0.5 * X[, 8]) +
rnorm(n)
fit3 <- ATE(Y,treat,X)
summary(fit3)#> Call:
#> ATE(Y = Y, treat = treat, X = X)
#>
#> Estimate Std. Error 95%.Lower 95%.Upper z value p value
#> E[Y(0)] -0.625055 0.114586 -0.849640 -0.400470 -5.4549 4.9e-08 ***
#> E[Y(1)] 10.559242 0.084657 10.393317 10.725168 124.7291 < 2e-16 ***
#> E[Y(2)] 22.231546 0.241661 21.757899 22.705194 91.9946 < 2e-16 ***
#> E[Y(3)] 33.240013 0.352811 32.548516 33.931510 94.2148 < 2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(fit3)
ATEuses the R packagesRcppandRcppArmadilloto improve run-time. This allows us to handle big data efficiently.
Below we present the example for 10,000 observations and 800 covariates on an Intel® Core™ i5-3337U Processor.
n <- 10000
X1 <- matrix(rnorm(n * 500), ncol = 500)
X2 <- matrix(rbinom(300 * n, 1, 0.4), ncol = 300)
X <- cbind(X1, X2)
prop <- 1 / (1 + exp( X[, 1] - 0.5 * X[, 2] + 0.25 * X[, 3] + X[, 6] + 0.5 * X[, 8]))
treat <- rbinom(n, 1, prop)
Y <- 10 * treat + (2 * treat - 1) *
(X[, 1] - 0.5 * X[, 2] + 0.25 * X[, 3] + X[, 6] + 0.5 * X[, 8]) +
rnorm(n)
system.time({
fit4 <- ATE(Y, treat, X)
})#> user system elapsed
#> 42.438 0.918 44.075
I would like to express my deep gratitude to Professor Gary Chan, my research supervisor, for his patient guidance, enthusiastic encouragement and useful critiques of this project.
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