gp_rdd

This function implements a regression discontinuity (RD) design using Gaussian Process (GP) regression on each side of the cutoff. Separate GP models are estimated for observations below and above the threshold, allowing for flexible and fully nonparametric functional forms. The treatment effect is computed as the difference between the predicted conditional means at the cutoff from the right and left limits. Standard errors and confidence intervals are constructed using the posterior predictive variance from the two independently fitted GPs.

Usage

gp_rdd(
  X,
  Y,
  cut,
  alpha = 0.05,
  b = NULL,
  trim = FALSE,
  trim_k_value = 0.1,
  scale = TRUE
)

Arguments

X

forcing variable

Y

Y vector (outcome variable)

cut

cut point

alpha

confidence level (default = 0.05)

b

bandwidth (default = NULL)

trim

a logical value indicating whether you want to do an automatic trim at a specific value of trim_k_value (default=FALSE)

trim_k_value

a numerical value indicating the kernel value that you want to trim above (default = 0.1)

scale

a logical value indicating whether you want to scale the covariates (default = TRUE)

Value

tau

an estimated treatment effect

se

the standard error of tau

Examples

n <- 100
tau <- 3
cut <- 0
x <- rnorm(n, 0, 1)
y <- rnorm(n, 0, 1) + tau*ifelse(x>cut, 1, 0)
gp_rdd.out <- gp_rdd(x, y, cut)
gp_rdd_plot(gp_rdd.out)