rdhte {rdhte}R Documentation

RD Heterogeneous Treatment Effects Estimation and Inference

Description

rdhte provides estimation and inference for heterogeneous treatment effects in RD designs using local polynomial regressions, allowing for interactions with pretreatment covariates (Calonico, Cattaneo, Farrell, Palomba and Titiunik, 2025a). Inference is implemented using robust bias-correction methods (Calonico, Cattaneo, and Titiunik, 2014)

Companion commands: rdbwhte for data-driven bandwidth selection and rdhte_lincom for testing linear restrictions of parameters.

Related software packages for analysis and interpretation of RD designs and related methods are available in: https://rdpackages.github.io/.

For background methodology, see Calonico, Cattaneo, Farrell, and Titiunik (2019), Calonico, Cattaneo and Farrell (2020), Cattaneo and Titiunik (2022).

Usage

rdhte(
  y,
  x,
  c = 0,
  covs.hte = NULL,
  covs.eff = NULL,
  p = 1,
  q = 2,
  kernel = "tri",
  weights = NULL,
  h = NULL,
  h.l = NULL,
  h.r = NULL,
  vce = "hc3",
  cluster = NULL,
  level = 95,
  bwselect = NULL,
  bw.joint = FALSE,
  subset = NULL
)

Arguments

y

Outcome variable.

x

Running variable.

c

RD cutoff in x; default is c = 0.

covs.hte

covariates for heterogeneous treatment effects. Factor variables can be used to distinguish between continuous and categorical variables, select reference categories, specify interactions between variables, and include polynomials of continuous variables. If not specified, the RD Average Treatment Effect is computed.

covs.eff

additional covariates to be used for efficiency improvements.

p

order of the local polynomial used to construct the point estimator (default = 1).

q

order of the local polynomial used to construct the bias correction (default = 2).

kernel

kernel function used to construct the RD estimators. Options are triangular (default option), epanechnikov and uniform.

weights

variable used for optional weighting of the estimation procedure. The unit-specific weights multiply the kernel function.

h

main bandwidth used to construct the RD estimator. If not specified, bandwidth h is computed by the companion command rdbwhte. More than one bandwidth can be specified for categorical covariates.

h.l

same as h, but only used for observations left of the cutoff c.

h.r

same as h, but only used for observations right of the cutoff c.

vce

character string specifying the variance-covariance matrix estimator type (hc0–hc3) (default = "hc3"). It is based on the R function vcovCL.

cluster

variable indicating the clustering of observations.

level

confidence level for confidence intervals; default is level = 95.

bwselect

bandwidth selection procedure to be used. Options are: mserd one common MSE-optimal bandwidth selector for the RD treatment effect estimator. msetwo two different MSE-optimal bandwidth selectors (below and above the cutoff) for the RD treatment effect estimator. msesum one common MSE-optimal bandwidth selector for the sum of regression estimates (as opposed to difference thereof). msecomb1 for min(mserd,msesum). msecomb2 for median(msetwo,mserd,msesum), for each side of the cutoff separately. cerrd one common CER-optimal bandwidth selector for the RD treatment effect estimator. certwo two different CER-optimal bandwidth selectors (below and above the cutoff) for the RD treatment effect estimator. cersum one common CER-optimal bandwidth selector for the sum of regression estimates (as opposed to difference thereof). cercomb1 for min(cerrd,cersum). cercomb2 for median(certwo,cerrd,cersum), for each side of the cutoff separately. Note: MSE = Mean Square Error; CER = Coverage Error Rate. Default is bwselect=mserd.

bw.joint

logical. If TRUE, forces all bandwidths to be the same across groups (default is bw.joint = FALSE).

subset

optional vector specifying a subset of observations to be used.

Value

A list with selected RD HTE effects and model information.

Estimate

vector of conventional local-polynomial RD estimates.

Estimate.bc

vector of bias-corrected local-polynomial RD estimates.

se.rb

vector containing robust bias corrected standard errors of the local-polynomial RD estimates.

ci.rb

matrix containing robust bias corrected confidence intervals.

t.rb

vector containing the t-statistics associated with robust local-polynomial RD estimates.

pv.rb

vector containing the p-values associated with robust local-polynomial RD estimates.

coefs

vector containing the coefficients for the jointly estimated p-th order local polynomial model.

vcov

estimated variance-covariance matrix.

W.lev

vector of group level identifiers.

kernel

kernel type used.

vce

variance estimator used.

c

cutoff value.

h

vector containing the bandwidths used.

p

order of the polynomial used for estimation of the regression function.

q

order of the polynomial used for inference on the regression function.

N

vector with the original number of observations for each group.

Nh

vector with the effective number of observations for each group.

covs.cont

internal value.

level

confidence level used.

rdmodel

rd model.

Author(s)

Sebastian Calonico, University of California, Davis scalonico@ucdavis.edu.

Matias D. Cattaneo, Princeton University cattaneo@princeton.edu.

Max H. Farrell, University of California, Santa Barbara maxhfarrell@ucsb.edu.

Filippo Palomba, Princeton University fpalomba@princeton.edu.

Rocio Titiunik, Princeton University titiunik@princeton.edu.

References

Calonico, Cattaneo, Farrell, Palomba and Titiunik (2025): rdhte: Learning Conditional Average Treatment Effects in RD Designs. Working paper.

Calonico, Cattaneo, Farrell, Palomba and Titiunik (2025): Treatment Effect Heterogeneity in Regression Discontinuity Designs. Working paper.

Cattaneo, Farrell, and Titiunik. 2022. Regression Discontinuity Designs. Annual Review of Economics, 14: 821-851.

Calonico, Cattaneo, and Farrell. 2020. Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs. Econometrics Journal, 23(2): 192-210.

Calonico, Cattaneo, Farrell, and Titiunik. 2019. Regression Discontinuity Designs using Covariates. Review of Economics and Statistics, 101(3): 442-451.

Calonico, Cattaneo, and Titiunik. 2014a. Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs. Econometrica 82(6): 2295-2326.

See Also

rdbwhte, rdhte_lincom

Examples

set.seed(123)
n <- 1000
X <- runif(n, -1, 1)
W <- rbinom(n, 1, 0.5)
Y <- 3 + 2*X + 1.5*X^2 + 0.5*X^3 + sin(2*X) + 3*W*(X>=0) + rnorm(n)
m1 = rdhte(y = Y, x = X, covs.hte = factor(W))
summary(m1)

[Package rdhte version 0.1.0 Index]