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.
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AD cases and non-AD controls were matched 1:1 on age, sex, and race/ethnicity using propensity score matching.
“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]
Directed acyclic graphs were used 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.
“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
The study imposed the common support condition to ensure the quality of the matches.
“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
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- 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 › Decision Sciences › Management Science and Operations Research › 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
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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.
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