Created
June 14, 2022 22:29
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nonparam.R
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rm(list = ls()) | |
library(grf) | |
coeffs = replicate(250, { | |
# setup from | |
# https://tompepinsky.com/2022/06/07/illustrating-contamination-bias-with-implications-for-intersectional-description/ | |
effect1 = 1 | |
effect2 = 2 | |
effect3 = 3 | |
n_w = 500 | |
t <- c( | |
sample(c(0, 1, 2), size = n_w, replace=TRUE, prob = c(.1,.4,.5)), | |
sample(c(0, 1, 2), size = n_w, replace=TRUE, prob = c(1/3,1/3,1/3)), | |
sample(c(0, 1, 2), size = n_w, replace=TRUE, prob = c(.5,.4,.1)) | |
) | |
modelmat <- data.frame(rep(NA,3*n_w),t,c(rep(1,n_w),rep(2,n_w),rep(3,n_w))) | |
names(modelmat) <- c("y","t","w") | |
modelmat$y[modelmat$w==1 & modelmat$t==0] <- 0 + rnorm(length(modelmat$y[modelmat$w==1 & modelmat$t==0]),0,3) | |
modelmat$y[modelmat$w==1 & modelmat$t==1] <- 0 + rnorm(length(modelmat$y[modelmat$w==1 & modelmat$t==1]),0,3) | |
modelmat$y[modelmat$w==1 & modelmat$t==2] <- effect1 + rnorm(length(modelmat$y[modelmat$w==1 & modelmat$t==2]),0,3) | |
modelmat$y[modelmat$w==2 & modelmat$t==0] <- 0 + rnorm(length(modelmat$y[modelmat$w==2 & modelmat$t==0]),0,3) | |
modelmat$y[modelmat$w==2 & modelmat$t==1] <- 0 + rnorm(length(modelmat$y[modelmat$w==2 & modelmat$t==1]),0,3) | |
modelmat$y[modelmat$w==2 & modelmat$t==2] <- effect2 + rnorm(length(modelmat$y[modelmat$w==2 & modelmat$t==2]),0,3) | |
modelmat$y[modelmat$w==3 & modelmat$t==0] <- 0 + rnorm(length(modelmat$y[modelmat$w==3 & modelmat$t==0]),0,3) | |
modelmat$y[modelmat$w==3 & modelmat$t==1] <- 0 + rnorm(length(modelmat$y[modelmat$w==3 & modelmat$t==1]),0,3) | |
modelmat$y[modelmat$w==3 & modelmat$t==2] <- effect3 + rnorm(length(modelmat$y[modelmat$w==3 & modelmat$t==2]),0,3) | |
ols = summary(lm(y~factor(t)+factor(w), data=modelmat))$coefficients | |
cf = multi_arm_causal_forest(modelmat[, "w", drop = F], modelmat[, "y"], as.factor(modelmat[, "t"])) | |
ate = average_treatment_effect(cf) | |
c( | |
ols = ols[2, 1], | |
cf = ate[1, 1], # Contrast arm 1 - 0 should be zero. | |
cf.se = ate[1, 2] | |
) | |
}) | |
par(mfrow = c(2, 1)) | |
hist(coeffs["ols",], main = "OLS") | |
abline(v = 0, col='red') | |
hist(coeffs["cf",], main = "GRF (Multi-arm Causal Forest)") | |
abline(v = 0, col='red') | |
sd(coeffs["cf",]) | |
# [1] 0.2142025 | |
mean(coeffs["cf.se", ]) | |
# [1] 0.2180095 |
Author
erikcs
commented
Jun 14, 2022
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