The DOI matches its Crossref record; 108 of the 111 references it cites were found in a registry or an index.
SOME PRACTICAL GUIDANCE FOR THE IMPLEMENTATION OF PROPENSITY SCORE MATCHING
Abstract
Propensity score matching (PSM) has become a popular approach to estimate causal treatment effects. It is widely applied when evaluating labour market policies, but empirical examples can be found in very diverse fields of study. Once the researcher has decided to use PSM, he is confronted with a lot of questions regarding its implementation. To begin with, a first decision has to be made concerning the estimation of the propensity score. Following that one has to decide which matching algorithm to choose and determine the region of common support. Subsequently, the matching quality has to be assessed and treatment effects and their standard errors have to be estimated. Furthermore, questions like ‘what to do if there is choice‐based sampling?’ or ‘when to measure effects?’ can be important in empirical studies. Finally, one might also want to test the sensitivity of estimated treatment effects with respect to unobserved heterogeneity or failure of the common support condition. Each implementation step involves a lot of decisions and different approaches can be thought of. The aim of this paper is to discuss these implementation issues and give some guidance to researchers who want to use PSM for evaluation purposes.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“To minimize confounding bias, AD cases and non-AD controls were matched 1:1 on age, sex, and race/ethnicity using propensity score matching (37).”
From Alzheimer's Disease–Associated Amyloid-β Precursor Protein Prevents Aging Stress–Induced Mitophagy and Fumarate Depletion to Improve Antitumor Immunity (2025) · cites this paper as [37]
In the citing paper
“We used directed acyclic graphs to identify factors that could be associated with both reading for pleasure and children's strengths and difficulties, or with children's strengths and difficulties only based on previous empirical research (Brookhart et al., 2006; Caliendo and Kopeinig, 2008; Rubin, 2001; Sauer and VanderWeele, 2013).”
From Longitudinal associations between reading for pleasure and child maladjustment: Results from a propensity score matching analysis (2020) · cites this paper as Caliendo and Kopeinig, 2008
In the citing paper
“We imposed the common support condition to ensure the quality of the matches (Caliendo and Kopeinig, 2008).”
From Longitudinal associations between reading for pleasure and child maladjustment: Results from a propensity score matching analysis (2020) · cites this paper as Caliendo and Kopeinig, 2008
Citations
Cites
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
(2001) · RePEc: Research Papers in Economics · Cited by 27,577
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Density Estimation for Statistics and Data Analysis
Silverman, B. W. (2018) · Routledge eBooks · doi:10.1201/9781315140919 · Cited by 15,938
Statistical Aspects of the Analysis of Data From Retrospective Studies of Disease
(1959) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/22.4.719 · Cited by 14,689
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · Journal of Educational Psychology · doi:10.1037/h0037350 · Cited by 9,436
Density Estimation for Statistics and Data Analysis
(1986) · Springer eBooks · doi:10.1007/978-1-4899-3324-9 · Cited by 8,569
Becker, G. S. (1983) · Cited by 6,708
Rosenbaum, P. R., & Rubin, D. B. (1985) · The American Statistician · doi:10.2307/2683903 · Cited by 6,472
Research works printed but not linked: 3
- Greene W.H., 2003, Econometric Analysis
- 10.2307/1390693 · DOI
- Lechner M., 1998, Qualifikation, Weiterbildung und Arbeitsmarkterfolg. ZEW‐Wirtschaftsanalysen Band 31
Cited by
Sorted by citations
Why Propensity Scores Should Not Be Used for Matching
King, G., & Nielsen, R. (2019) · Political Analysis · doi:10.1017/pan.2019.11 · Cited by 1,706
Chrisman, J. J., & Patel, P. C. (2012) · Academy of Management Journal · doi:10.5465/amj.2011.0211 · Cited by 1,468
Capacity shortfalls hinder the performance of marine protected areas globally
Gill, D., Mascia, M. B., Ahmadia, G. N., Glew, L., Lester, S. E., Barnes, M. D., Craigie, I. D., Darling, E. S., Free, C. M., Geldmann, J., et al. (2017) · Nature · doi:10.1038/nature21708 · Cited by 955
Methods for Constructing and Assessing Propensity Scores
Garrido, M. M., Kelley, A. S., Paris, J., Roza, K. A., Meier, D. E., Morrison, R. S., & Aldridge, M. D. (2014) · Health Services Research · doi:10.1111/1475-6773.12182 · Cited by 925
The economic impact of Special Economic Zones: Evidence from Chinese municipalities
Wang, J. (2013) · Journal of Development Economics · doi:10.1016/j.jdeveco.2012.10.009 · Cited by 880
Metadata
- Authors
- Caliendo, M., & Kopeinig, S.
- Affiliations
- IZA - Institute of Labor Economics (DE)
- ORCID
- Caliendo, Marco
- Venue
- Journal of Economic Surveys
- Publisher
- Wiley
- Published
- 2008-01-31
- Volume
- 22
- Issue
- 1
- Pages
- 31-72
- ISSN
- 0950-0804 (ISSN-L)
- 1467-6419
- OpenAlex
- W2159805452
- Cited by
- 6,238
- References
- 111
- Citations by year
- 2026: 382 · 2025: 443 · 2024: 428 · 2023: 426 · 2022: 459 · 2021: 455 · 2020: 430 · 2019: 401 · 2018: 396 · 2017: 362 · 2016: 341 · 2015: 338 · 2014: 367 · 2013: 261 · 2012: 234
- FWCI
- 143.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Economic Surveys (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Public Administration › Evaluation and Performance Assessment
- Social Sciences › Education › School Choice and Performance
- Keywords
- propensity score matching, causal treatment effects, common support, choice-based sampling, unobserved heterogeneity, sensitivity analysis, labour market policy evaluation
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
