Regression Discontinuity Designs as Local Randomized Experiments
Overview
A set of functions for the design and analysis of Regression Discontinuity Designs as local randomized experiments within the potential outcome approach as formalized in Li, Mattei and Mealli (2015) doi:10.1214/15-AOAS809. A subset of functions implements the design phase of the study, where the focus is on the selection of suitable subpopulations for which valid causal inference can be drawn. These functions provide summary statistics of pre- and post-treatment variables by treatment status and select suitable subpopulations around the threshold where pre-treatment variables are well balanced between treatment groups, using randomization-based tests with adjustment for multiplicities. Functions for a visual inspection of the results are also provided. Finally, the package includes a set of functions for drawing inference on causal effects for the selected subpopulations using randomization-based modes of inference. Specifically, the Fisher Exact p-value and Neyman approaches are implemented for the analysis of both sharp and fuzzy Regression Discontinuity designs. The approach is illustrated in a study concerning the effects of university grants on student dropout.
Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK7 OK2026-08-07
Documentation
- Help pages
- 48 (2 internal)
- Examples that run
- 33%2 run in a plain check (1 of them under a condition) · 4 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 13%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 2%
Downloads
Dependencies
Code & Tests
Datasets
People & History
- 0.1.0Latest2026-08-06 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-08-06
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.5
- Bundled data
- 95 KB / 1 file
- Download size
- 490 KB
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