rdrandinf: Randomization Inference for RD Designs under Local...

In rdlocrand: Local Randomization Methods for RD Designs

Randomization Inference for RD Designs under Local Randomization

Description

rdrandinf implements randomization inference and related methods for RD designs, using observations in a specified or data-driven selected window around the cutoff where local randomization is assumed to hold.

Usage

rdrandinf(
  Y,
  R,
  cutoff = 0,
  wl = NULL,
  wr = NULL,
  statistic = "diffmeans",
  p = 0,
  evall = NULL,
  evalr = NULL,
  kernel = "uniform",
  fuzzy = NULL,
  nulltau = 0,
  d = NULL,
  dscale = NULL,
  ci,
  interfci = NULL,
  bernoulli = NULL,
  reps = 1000,
  seed = 666,
  quietly = FALSE,
  covariates,
  obsmin = NULL,
  wmin = NULL,
  wobs = NULL,
  wstep = NULL,
  wasymmetric = FALSE,
  wmasspoints = FALSE,
  nwindows = 10,
  dropmissing = FALSE,
  rdwstat = "diffmeans",
  approx = FALSE,
  rdwreps = 1000,
  level = 0.15,
  plot = FALSE,
  firststage = FALSE,
  obsstep = NULL
)

Arguments

Y	a vector containing the values of the outcome variable.
R	a vector containing the values of the running variable.
cutoff	the RD cutoff (default is 0).
wl	the left limit of the window. The default takes the minimum of the running variable.
wr	the right limit of the window. The default takes the maximum of the running variable.
statistic	the randomization test statistic to be used. Allowed options are diffmeans (difference in means statistic), ksmirnov (Kolmogorov-Smirnov statistic), ranksum (Wilcoxon-Mann-Whitney standardized statistic), and all. Default option is diffmeans. The statistic ttest is equivalent to diffmeans and included for backward compatibility.
p	the order of the polynomial for the outcome adjustment model (default is 0).
evall	the point to the left of the cutoff at which the adjusted outcome is evaluated. Default is the cutoff value.
evalr	the point to the right of the cutoff at which the adjusted outcome is evaluated. Default is the cutoff value.
kernel	specifies the type of kernel to use as a weighting scheme. Allowed kernel types are uniform (uniform kernel), triangular (triangular kernel), and epan (Epanechnikov kernel). Default is uniform.
fuzzy	indicates that the RD design is fuzzy. fuzzy can be specified as a vector containing the values of the endogenous treatment variable, or as a list where the first element is the vector of endogenous treatment values and the second element is a string containing the statistic to be used. Allowed statistics are ar or itt (Anderson-Rubin/intention-to-treat statistic) and tsls (2SLS statistic). Default statistic is ar. The tsls statistic relies on a large-sample approximation.
nulltau	the value of the treatment effect under the null hypothesis (default is 0).
d	the effect size for asymptotic power calculation. Default is 0.5 times the standard deviation of the outcome variable for the control group.
dscale	the fraction of the standard deviation of the outcome variable for the control group used as the alternative hypothesis for asymptotic power calculation. Default is 0.5.
ci	calculates a confidence interval for the treatment effect by test inversion. ci can be specified as a scalar or a vector, where the first element indicates the value of alpha for the confidence interval (typically 0.05 or 0.01) and the remaining elements, if specified, indicate the grid of treatment effects to be evaluated. This option uses rdsensitivity to calculate the confidence interval. See the corresponding help file for details. Note: the default tlist can be narrow in some cases, which may truncate the confidence interval. We recommend manually setting a large enough tlist.
interfci	the level for Rosenbaum's confidence interval under arbitrary interference between units.
bernoulli	the probabilities of treatment for each unit when assignment mechanism is a Bernoulli trial. This option should be specified as a vector of length equal to the length of the outcome and running variables.
reps	the number of replications (default is 1000).
seed	the seed to be used for the randomization test.
quietly	suppresses the output table.
covariates	the covariates used by rdwinselect to choose the window when wl and wr are not specified. This should be a matrix of size n x k where n is the total sample size and k is the number of covariates.
obsmin	the minimum number of observations above and below the cutoff in the smallest window used by the companion command rdwinselect. Default is 10.
wmin	the smallest window to be used (if obsmin is not specified) by the companion command rdwinselect. Specifying both wmin and obsmin returns an error.
wobs	the number of observations to be added on each side of the cutoff at each step.
wstep	the increment in window length (if obsstep is not specified) by the companion command rdwinselect. Specifying both obsstep and wstep returns an error.
wasymmetric	allows for asymmetric windows around the cutoff when wobs is specified.
wmasspoints	specifies that the running variable is discrete and each masspoint should be used as a window.
nwindows	the number of windows to be used by the companion command rdwinselect. Default is 10.
dropmissing	drop rows with missing values in covariates when calculating windows.
rdwstat	the statistic to be used by the companion command rdwinselect (see the corresponding help file for options). Default option is diffmeans.
approx	forces the companion command rdwinselect to conduct the covariate balance tests using a large-sample approximation instead of finite-sample exact randomization inference methods.
rdwreps	the number of replications to be used by the companion command rdwinselect. Default is 1000.
level	the minimum accepted value of the p-value from the covariate balance tests to be used by the companion command rdwinselect. Default is .15.
plot	draws a scatter plot of the minimum p-value from the covariate balance test against window length implemented by the companion command rdwinselect.
firststage	reports the results from the first step when using tsls.
obsstep	the minimum number of observations to be added on each side of the cutoff for the sequence of fixed-increment nested windows. Default is 2. This option is deprecated and only included for backward compatibility.

Value

A list containing:

sumstats	matrix of full-sample and window-specific summary statistics.
obs.stat	observed statistic or statistics.
p.value	randomization p-value or p-values.
asy.pvalue	asymptotic p-value or p-values.
window	chosen window endpoints.
ci	confidence interval; included only when ci is specified.
interf.ci	confidence interval under interference; included only when interfci is specified.

Author(s)

Matias D. Cattaneo, Princeton University. [e-mail]

Rocio Titiunik, Princeton University. [e-mail]

Gonzalo Vazquez-Bare, UC Santa Barbara. [e-mail]

References

Cattaneo, M.D., B. Frandsen and R. Titiunik. (2015). Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate. Journal of Causal Inference 3(1): 1-24.

Cattaneo, M.D., R. Titiunik and G. Vazquez-Bare. (2016). Inference in Regression Discontinuity Designs under Local Randomization. Stata Journal 16(2): 331-367.

Cattaneo, M.D., R. Titiunik and G. Vazquez-Bare. (2017). Comparing Inference Approaches for RD Designs: A Reexamination of the Effect of Head Start on Child Mortality. Journal of Policy Analysis and Management 36(3): 643-681.

Examples

# Toy dataset
set.seed(123)
X <- array(rnorm(200),dim=c(100,2))
R <- X[,1] + X[,2] + rnorm(100)
Y <- 1 + R -.5*R^2 + .3*R^3 + (R>=0) + rnorm(100)
# Randomization inference in window (-.75,.75)
tmp <- rdrandinf(Y,R,wl=-.75,wr=.75,quietly=TRUE)
# Randomization inference in window (-.75,.75), all statistics
tmp <- rdrandinf(Y,R,wl=-.75,wr=.75,statistic='all',quietly=TRUE)
# Randomization inference with window selection
# Note: low number of replications to speed up process.
# The user should increase the number of replications.
tmp <- rdrandinf(Y,R,statistic='all',covariates=X,wmin=.5,wstep=.125,
                 rdwreps=500,level=0,quietly=TRUE)

rdlocrand documentation built on May 14, 2026, 5:10 p.m.