rdlearn: Safe Policy Learning for Regression Discontinuity Design with...

In rdlearn: Safe Policy Learning under Regression Discontinuity Design with Multiple Cutoffs

Safe Policy Learning for Regression Discontinuity Design with Multiple Cutoffs

Description

The rdlearn function implements safe policy learning under a regression discontinuity design with multiple cutoffs. The resulting new treatment assignment rules (cutoffs) are guaranteed to yield no worse overall outcomes than the existing cutoffs.

Usage

rdlearn(
  y,
  x,
  c,
  group_name = NULL,
  data,
  fold = 10,
  M = 1,
  cost = 0,
  trace = TRUE
)

Arguments

y	A character string specifying the name of column containing the outcome variable.
x	A character string specifying the name of column containing the running variable.
c	A character string specifying the name of column containing the cutoff variable.
group_name	A character string specifying the name of the column containing group names (e.g., department names) for each cutoff. If not provided, the groups are assigned names "Group 1", "Group 2", ... in ascending order of cutoff values.
data	A data frame containing all required variables.
fold	The number of folds for cross-fitting. Default is 10.
M	A numeric value or vector specifying the multiplicative smoothness factor(s) for sensitivity analysis. Default is 1.
cost	A numeric value or vector specifying the cost of treatment for calculating regret. This cost should be scaled by the range of the outcome variable Y. Default is 0.
trace	A logical value that controls whether to display the progress. If set to TRUE, the progress will be printed. The default value is TRUE.

Details

Regarding the detail of the algorithm, please refer to Zhang et al. (2022) "4 Empirical policy learning" and "A.2 A double robust estimator for heterogeneous cross-group differences".

Value

An object of class rdlearn, which is a list containing the following components:

call

The original function call.

var_names

A list of variable names for the outcome, running variable, and cutoff.

org_cut

A vector of original cutoff values.

safe_cut

A data frame containing the obtained new treatment assignment cutoffs.

sample

The total sample size.

num_group

The number of groups.

group_name

A vector of group names.

cross_fit_output

The intermediate output of the cross-fitting procedure.

dif_lip_output

The intermediate output of the cross-group differences and the smoothness parameters

distance

A numeric vector containing the measures of difference between safe cutoffs and original cutoffs

rdestimates

A data frame containing the result of rdesimate such as causal effect estimates.

temp_reg_df

A data frame containing the regrets of every alternative cutoff.

Examples

# Simulation Data B from Appendix D of Zhang et al. (2022)
set.seed(1)
n <- 300
X <- runif(n, -1000, -1)
G <- 2 * as.numeric(
I(0.01 * X + 5 + rnorm(n, sd = 10) > 0)
) +
as.numeric(
I(0.01 * X + 5 + rnorm(n, sd = 10) <= 0)
)
c1 <- -850
c0 <- -571
C <- ifelse(G == 1, c1, c0)
D <- as.numeric(X >= C)
coef0 <- c(-1.992230e+00, -1.004582e-02, -1.203897e-05, -4.587072e-09)
coef1 <- c(9.584361e-01, 5.308251e-04, 1.103375e-06, 1.146033e-09)
Px <- poly(X, degree = 3, raw = TRUE)
# Px = poly(X-735.4334-c1,degree=3,raw=TRUE) for Simulation A
Px <- cbind(rep(1, nrow(Px)), Px)
EY0 <- Px %*% coef0
EY1 <- Px %*% coef1
d <- 0.2 + exp(0.01 * X) * (1 - G) + 0.3 * (1 - D)
Y <- EY0 * (1 - D) + EY1 * D - d * as.numeric(I(G == 1)) + rnorm(n, sd = 0.3)

simdata_B_demo <- data.frame(Y,X,C)

# Learn new treatment assignment cutoffs
rdlearn_result <- rdlearn(
  y = "Y", x = "X", c = "C", data = simdata_B_demo,
  fold = 2, M = 0, cost = 0
)

# Summarise the learned policies
summary(rdlearn_result)

# Visualize the learned policies
plot(rdlearn_result, opt = "dif")
# The learned cutoff for Group 1 is the same as the baseline cutoff, because
# the baseline cutoff is set to equal to oracle cutoff in this simulation.

# Implement sensitivity analysis
sens_result <- sens(rdlearn_result, M = 1, cost = 0)
plot(sens_result, opt = "dif")

rdlearn documentation built on April 4, 2025, 5:17 a.m.