The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A Practical Introduction to Regression Discontinuity Designs: Foundations
Open accessAbstract
In this Element and its accompanying second Element, A Practical Introduction to Regression Discontinuity Designs: Extensions, Matias Cattaneo, Nicolás Idrobo, and Rocıìo Titiunik provide an accessible and practical guide for the analysis and interpretation of regression discontinuity (RD) designs that encourages the use of a common set of practices and facilitates the accumulation of RD-based empirical evidence. In this Element, the authors discuss the foundations of the canonical Sharp RD design, which has the following features: (i) the score is continuously distributed and has only one dimension, (ii) there is only one cutoff, and (iii) compliance with the treatment assignment is perfect. In the second Element, the authors discuss practical and conceptual extensions to this basic RD setup.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Regression discontinuity designs in economics
Lee, D. S., & Lemieux, T. (2010) · Journal of Economic Literature · doi:10.1257/jel.48.2.281 · Cited by 4,534
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Imbens, G. W., & Rubin, D. B. (2015) · doi:10.1017/cbo9781139025751 · Cited by 4,210
Regression-discontinuity analysis: An alternative to the ex post facto experiment.
Thistlethwaite, D. L., & Campbell, D. T. (1960) · Journal of Educational Psychology · doi:10.1037/h0044319 · Cited by 1,469
Design of Observational Studies
Rosenbaum, P. R. (2010) · Springer Series in Statistics · doi:10.1007/978-1-4419-1213-8 · Cited by 1,150 · cited 1×
“The importance of falsification tests and the use of placebo outcomes is generally discussed in the analysis of experiments literature (e.g., Imbens and Rubin, 2015; Rosenbaum, 2002, 2010).”
MethodWing, C., & Cook, T. D. (2013) · Journal of Policy Analysis and Management · doi:10.1002/pam.21721 · Cited by 87 · cited 2×
“…of RD treatment effects can be done by imposing additional assumptions about 18 (i) the regression functions near the cutoff (Dong and Lewbel, 2015; Wing and Cook, 2013), (ii) local independence assumptions (Angrist and Rokkanen, 2015) (iii) exploiting specific features of the design such as…”
Background
Cited by
RENY, T. T., & NEWMAN, B. J. (2021) · American Political Science Review · doi:10.1017/s0003055421000460 · Cited by 342
Causal Inference About the Effects of Interventions From Observational Studies in Medical Journals
Dahabreh, I. J., & Bibbins-Domingo, K. (2024) · JAMA · doi:10.1001/jama.2024.7741 · Cited by 242
A Costly Commitment: Populism, Economic Performance, and the Quality of Bureaucracy
Bellodi, L., Morelli, M., & Vannoni, M. (2024) · American Journal of Political Science · doi:10.1111/ajps.12782 · Cited by 68
Geographic difference-in-discontinuities
Butts, K. (2023) · Applied Economics Letters · doi:10.1080/13504851.2021.2005236 · Cited by 41
Metadata
- Authors
- Cattaneo, M. D., Idrobo, N., & Titiunik, R.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2019-11-16
- OpenAlex
- W3098283733
- Cited by
- 545
- References
- 5
- FWCI
- 20.42
- Type
- book
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Practical Introduction to Regression Discontinuity Designs: Foundations. https://app.cochat.ai/papers/10.1017/9781108684606
- The paper
- Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2019). A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge University Press eBooks. https://doi.org/10.1017/9781108684606
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
