Maximum entropy in the mean methods in propensity score matching for interval and noisy data
In this paper, we propose maximum entropy in the mean methods for propensity score matching classification problems. We provide a new methodological approach and estimation algorithms to handle explicitly cases when data is available: (i) in interval form; (ii) with bounded measurement or observatio...
| Autores: | , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | Colombia |
| Institución: | Universidad de los Andes |
| Repositorio: | Séneca: repositorio Uniandes |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uniandes.edu.co:1992/47072 |
| Acceso en línea: | http://hdl.handle.net/1992/47072 https://www.tandfonline.com/doi/abs/10.1080/03610926.2018.1497656 |
| Access Level: | acceso abierto |
| Palabra clave: | Propensity score matching Observational studies Maximum entropy in the mean Data with bounded errors Interval data |
| Sumario: | In this paper, we propose maximum entropy in the mean methods for propensity score matching classification problems. We provide a new methodological approach and estimation algorithms to handle explicitly cases when data is available: (i) in interval form; (ii) with bounded measurement or observational errors; or (iii) both as intervals and with bounded errors. We show that entropy in the mean methods for these three cases generally outperform benchmark error-free approaches. |
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