Method library WORKFLOW / 029

Research workflow/4 steps

Sharp regression discontinuity with robust bias correction (rdrobust)

@rdrobust ✓ D · Jun 29, 2026 · 922 views

Source rdrobust — Calonico, Cattaneo, Farrell & Titiunik

@misc{rdrobust,
  title        = {rdrobust},
  author       = {Calonico and Cattaneo and Farrell and Titiunik},
  howpublished = {\url{https://rdpackages.github.io/rdrobust/}},
  note         = {Software / documentation}
}

In the margins · Peer notesA question, a caveat, a different perspective. Start a conversation here. 0

The method workspace

Follow the analysis.

Select a node to explore the method

Method mapInput → 4 steps → output

START WITHInput data

ARRIVE ATResults

Read step 01

Inside the methodReasoning, equations & implementation

Data prep

Running variable, cutoff, outcome

Step notesField note & discussion1

Data preparation — shapes the raw inputs into what the estimator expects.

What happens here

Set up X, the threshold c, and Y. Identification rests on continuity at c.

Formula

\tau_{\mathrm{RD}}=\lim_{x\downarrow c}\mathbb{E}[Y\mid X=x]-\lim_{x\uparrow c}\mathbb{E}[Y\mid X=x]

Reads fromNot specified Feeds intoNot specified

Key code

library(rdrobust)
# x = running variable, c = cutoff, y = outcome

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Diagnostic / pre-tests

rdplot — see the jump

Step notesField note & discussion1

A pre-flight check — run this before trusting any estimate downstream.

What happens here

Binned means on each side reveal whether there's a discontinuity to estimate.

Formula

\hat\mu_\pm(x)=\textstyle\sum_b \bar Y_b\,\mathbb{1}\{x\in \text{bin }b\}

Reads fromNot specified Feeds intoNot specified

Key code

rdplot(y, x, c = 0)

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Estimation

Local-linear RD with bias correction

Step notesField note & discussion1

The core estimate — where the causal quantity itself is computed.

What happens here

Fit local polynomials on each side at an MSE-optimal bandwidth; robust CIs.

Formula

h_{\mathrm{MSE}}=\arg\min_h\ \mathrm{MSE}\big(\hat\tau_{\mathrm{RD}}(h)\big)

Reads fromNot specified Feeds intoNot specified

Key code

rdrobust(y, x, c = 0)

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Robustness check

Bandwidth & donut sensitivity

Step notesField note & discussion1

A robustness check — does the headline result survive a different lens?

What happens here

Re-estimate across bandwidths and dropping points at the cutoff.

Formula

\hat\tau_{\mathrm{RD}}(h)\ \text{stable across }h \Rightarrow \text{credible}

Reads fromNot specified Feeds intoNot specified

Key code

rdrobust(y, x, c = 0, h = c(h1, h2))

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Data contract

Inputs & data

A running variable X with a known cutoff c, and an outcome Y; treatment switches on at X ≥ c.

Show data format & exampleHide example

Format — one row per unit: running variable x, outcome y (cutoff c).

   x      y
 -0.8   2.1
  0.3   4.6   # x ≥ c → treated
 -0.1   2.9

Read the evidence

Results & interpretation

Workflow discussion 0

Context & interpretation

⚠️ Unofficial community showcase of rdrobust. Not affiliated with the authors; all credit to them.

Identify the effect at a cutoff: a local-polynomial RD with an MSE-optimal bandwidth and robust, bias-corrected confidence intervals.

Follow the analysis.

Inputs & data

Results & interpretation

Discuss the workflow (0)