rdhte: RD Heterogeneous Treatment Effects Estimation and Inference

In rdhte: Heterogeneous Treatment Effects in Regression Discontinuity Designs

RD Heterogeneous Treatment Effects Estimation and Inference

Description

rdhte provides estimation and inference for heterogeneous treatment effects in RDD using local polynomial regression allowing for interactions with pretreatment covariates. Inference is implemented using robust bias-correction methods.

Companion commands: rdbwhte for data-driven bandwidth selection.

Related Stata and R packages useful for inference in RD designs are described in the website: https://rdpackages.github.io/.

Usage

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

Arguments

y	Outcome variable.
x	Running variable.
c	Cutoff value (default = 0).
covs.hte	Covariate(s) for heterogeneous treatment effects (required).
covs.eff	Additional covariates for efficiency (optional).
p	Polynomial order (default = 1).
kernel	Kernel type (default = "tri").
h	Choice of bandwidth (optional).
vce	Variance estimator (default = "hc3").
cluster	Optional cluster variable.
level	Confidence level (default = 95).
bw.joint	Logical, use joint bandwidth selection (default = FALSE).

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.
N	vector with the original number of observations for each group.
Nh	vector with the effective number of observations for each group.
coef_report	internal value.
level	confidence level used.
rdmodel	rd model.

Author(s)

Sebastian Calonico, University of California, Davis [e-mail].

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

Max H. Farrell, University of California, Santa Barbara [e-mail].

Filippo Palomba, Princeton University [e-mail].

Rocio Titiunik, Princeton University [e-mail].

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

See Also

rdbwhte

Examples

set.seed(123)
n <- 5000
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)
rdhte.1 = rdhte(y=Y, x=X, covs.hte=factor(W))
summary(rdhte.1)

rdhte documentation built on April 12, 2025, 1:49 a.m.