RD Bandwidth Sensitivity Analysis

Runs rdrobust over a grid of bandwidths and plots point estimates with confidence intervals against bandwidth values. This helps assess how sensitive the RD treatment effect estimate is to the choice of bandwidth.

rd_bandwidth_sensitivity(
  data,
  y,
  x,
  c = 0,
  bw_multiples = seq(0.5, 2, 0.1),
  kernel = "tri",
  p = 1,
  conf_level = 0.95,
  theme_use = causalverse::ama_theme()
)

Arguments

data

A data frame containing the outcome and running variable.

y

Character string. Name of the outcome variable.

x

Character string. Name of the running variable.

c

Numeric. The cutoff value for the RD design. Default is 0.

bw_multiples

Numeric vector. Multipliers applied to the MSE-optimal bandwidth from rdrobust. Default is seq(0.5, 2, 0.1).

kernel

Character string. Kernel function for rdrobust. Default is "tri" (triangular).

p

Integer. Order of the local polynomial. Default is 1.

conf_level

Numeric. Confidence level for intervals. Default is 0.95.

theme_use

A ggplot2 theme to apply to the plot. Default is causalverse::ama_theme().

Value

A list with components:

results

A data frame with columns: bandwidth, bw_multiple, estimate, std_error, ci_lower, ci_upper, p_value, n_left, n_right.

plot

A ggplot2 object showing estimates and CIs across bandwidths.

optimal_bw

The MSE-optimal bandwidth from rdrobust.

References

Calonico, S., Cattaneo, M. D., and Titiunik, R. (2014). "Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs." Econometrica, 82(6), 2295-2326.

Examples

if (FALSE) { # \dontrun{
set.seed(42)
n <- 1000
x <- runif(n, -1, 1)
y <- 3 + 2 * (x >= 0) + 0.5 * x + rnorm(n)
df <- data.frame(y = y, x = x)

result <- rd_bandwidth_sensitivity(
  data = df,
  y = "y",
  x = "x",
  c = 0,
  bw_multiples = seq(0.5, 2, 0.25)
)

result$plot
result$results
result$optimal_bw
} # }