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rdd-design

Estimate sharp and fuzzy regression discontinuity effects around a known cutoff.

33|6|Updated Mar 17, 2026
Search Tags:#python#causal inference#econometrics#policy evaluation#rdd#quasi-experiment#bandwidth selection
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill rdd-design
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rdd-design
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/07-economics/rdd-design
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill rdd-design

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, statsmodels, scipy, matplotlib, seaborn, rdrobust.

What problem does it solve?

This Skill helps you estimate causal effects from observational data using Regression Discontinuity Design (RDD) by leveraging a cutoff in a running variable to approximate a randomized experiment.

Core Features & Use Cases

  • Sharp RDD estimation: Compute the local treatment effect at the cutoff using bandwidth-weighted local polynomial regression, including standard errors, confidence intervals, and interpretation.
  • Fuzzy RDD (local IV) estimation: Estimate LATE by combining reduced-form and first-stage jumps at the cutoff, with uncertainty via a delta-method standard error.
  • Validity checks and robustness: Run McCrary-style manipulation (density continuity) tests, test covariate balance via separate RDDs on predetermined covariates, and assess bandwidth sensitivity to gauge estimator stability.
  • Visualization: Produce cutoff plots with binned means and local fits for transparent reporting.

Quick Start

Use the RDD skill to estimate the treatment effect at a known threshold from your outcome array y, running variable array x, and cutoff value c by asking your agent to run sharp and fuzzy RDD plus manipulation and bandwidth sensitivity checks on your dataset.

Frequently Asked Questions about rdd-design

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I estimate causal effects using regression discontinuity design in Python?▼

Estimate regression discontinuity design causal effects in Python by applying bandwidth-weighted local polynomial regressions around a known cutoff to calculate local treatment effects, standard errors, and confidence intervals.

What is the difference between sharp and fuzzy RDD estimation?▼

Sharp RDD calculates the local treatment effect directly at the cutoff using bandwidth-weighted local polynomial regression, while fuzzy RDD estimates the local average treatment effect by combining reduced-form and first-stage jumps with delta-method standard errors.

How do I check the validity of a regression discontinuity design?▼

Check regression discontinuity design validity by running McCrary-style manipulation tests for density continuity, testing covariate balance through separate RDDs on predetermined covariates, and assessing bandwidth sensitivity over a grid.

Can I use rdrobust with pandas and statsmodels for policy evaluation?▼

Yes, you can use rdrobust alongside pandas, statsmodels, and scipy to perform quasi-experimental policy evaluation, estimating discontinuous treatment assignment effects with robust standard errors and matplotlib visualizations.

How do I visualize regression discontinuity design results?▼

Visualize regression discontinuity design results by generating cutoff plots with binned means and local polynomial fits using matplotlib and seaborn, providing transparent graphical evidence for policy evaluation and econometric studies.

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