A prototype R library to calculate ML-based treatment effects per Varian (2014).
First, load in the code.
source("https://raw.githubusercontent.com/SigmaMonstR/ate/master/script/autoTrain.R")
Then simulate some fake data
#Set up data
n <- 10000
df <- data.frame(id = 1:n,
time = round((1:n)/100),
y = as.numeric(1:n + rnorm(n,30,300)),
x1 = rnorm(n, 100,100),
x2 = rnorm(n, 200,100),
x3 = runif(n)*(1:n),
x4 = rnorm(n, 100,100),
x5 = rnorm(n, 200,100),
x6 = runif(n)*(1:n))
df <- df[complete.cases(df),]
tt <- runif(nrow(df)) >= 0.6
Set up a parameter file:
args <- list(formula = as.formula("y ~ x1 + x2 + x3 + x4 + x5 + x6"),
data = df,
treatment = tt,
window.type = 2,
w.size = 10,
w.factor = 5,
algo = 2,
run.var = "time",
kval = 10,
rdd.thresh = 50)
Then apply:
out <- autoTrain(args)