Propensity Score Matching
By D. Peikes, Lorenzo Moreno, S. Orzol. 2008
287 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/ae0620ca16969370dffdefc19d68581f329d7320
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Conny Wunsch
- M. Lechner
- Judea Pearl
- Sheng Li
- Yun Raymond Fu
- Matthieu Clément
- Marco Caliendo
- Jochen Kluve
In Statistics and Probability
- M. Lechner
- Conny Wunsch
- Martin Huber
- Sheng Li
- Judea Pearl
- Yun Raymond Fu
- Matthieu Clément
- Markus Frölich
Citations in the corpus
- 3. The Foundations of Causal Inference (2010, background): “…Winship 2007; Berk and de Leeuw 1999), health scientists (Austin 2008), and economists (Heckman 1992) has increased the popularity of the method to the point where some federal agencies now expect program evaluators to use this approach as a substitute for experimental designs (Peikes et al. 2008).”
- Mining Electronic Health Records: A Survey (2017, unknown): “Statistical matching techniques such as propensity score matching (PSM) [Peikes et al. 2008] attempt to estimate the effect of a treatment or other intervention by accounting for the covariates that predict receiving the treatment.”
- Remittances and Household Expenditure Patterns in Tajikistan: A Propensity Score Matching Analysis (2011, background): “It was introduced by Rosembaum and Rubin (1983, 1985) and is defined as “an algorithm that matches treated and non participants on the basis of the conditional probability of participation (the propensity score), given the observable characteristics” (Essama-Nssah 2006, p. 5).”
- Sensitivity of Matching-Based Program Evaluations to the Availability of Control Variables (2013, background): “4 See also Heckman and Hotz (1989), Peikes, Moreno, and Orzol (2008), and Shadish, Clark, and Steiner (2008). credible selection correction in applications.”
- Matching on Balanced Nonlinear Representations for Treatment Effects Estimation (2017, methodology): “, the probability of receiving treatment) for each unit via logistic regression, and pairs the units from two groups with similar scores [35, 8, 30].”