package_rdlearn: Safe Policy Learning for Regression Discontinuity Designs

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

Safe Policy Learning for Regression Discontinuity Designs

Description

The rdlearn package provides tools for safe policy learning under regression discontinuity designs with multiple cutoffs.

Package Functions

The rdlearn package offers the following main functions:

Policy Learning

• rdlearn: Learn new treatment assignment cutoffs

Visualization

• plot: Visualize the learned cutoffs

Sensitivity Analysis

• sens: Perform sensitivity analysis

RD Estimate

• rdestimate: Estimate RD treatment effects

Summary

• summary: Summarize the result of rdlearn and rdestimate

This package also contains the ACCES Program data acces for replication of Section 6 of Zhang et al. (2022). We thank Tatiana Velasco and her coauthors for sharing the dataset (Melguizo et al. (2016)).

Author(s)

Maintainer: Kentaro Kawato [e-mail] [copyright holder]

Authors:

• Yi Zhang

• Soichiro Yamauchi

• Eli Ben-Michael

• Kosuke Imai

References

Zhang, Y., Ben-Michael, E. and Imai, K. (2022) 'Safe Policy Learning under Regression Discontinuity Designs with Multiple Cutoffs', arXiv [stat.ME]. Available at: http://arxiv.org/abs/2208.13323.

Melguizo, F., Sanchez, F., and Velasco, T. (2016) 'Credit for Low Income Students and Access to and Academic Performance in Higher Education in Colombia: A Regression Discontinuity Approach', World Development, 80(1): 61-77.

See Also

Useful links:

• https://github.com/kkawato/rdlearn

• Report bugs at https://github.com/kkawato/rdlearn/issues

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.