Quasi-experimental designs for causal inference: an overview-Reference-Cited by-同舟云学术

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Quasi-experimental designs for causal inference: an overview

Published:2024-06-26 Issue:3 Volume:25 Page:611-627
ISSN:1598-1037
Container-title:Asia Pacific Education Review
language:en
Short-container-title:Asia Pacific Educ. Rev.

Author:

Cham Heining^ORCID,Lee Hyunjung,Migunov Igor

Funder

National Institute on Aging

National Institute on Minority Health and Health Disparities

National Institute of Mental Health

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12564-024-09981-2.pdf

Reference58 articles.

1. Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91(434), 444–455. https://doi.org/10.1080/01621459.1996.10476902

2. Arpino, B., & Mealli, F. (2011). The specification of the propensity score in multilevel observational studies. Computational Statistics & Data Analysis, 55(4), 1770–1780. https://doi.org/10.1016/j.csda.2010.11.008

3. Austin, P. C. (2009). Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in Medicine, 28(25), 3083–3107. https://doi.org/10.1002/sim.3697

4. Austin, P. C. (2014). A comparison of 12 algorithms for matching on the propensity score. Statistics in Medicine, 33(6), 1057–1069. https://doi.org/10.1002/sim.6004

5. Baiocchi, M., Cheng, J., & Small, D. S. (2014). Tutorial in biostatistics: Instrumental variable methods for causal inference. Statistics in Medicine, 33(13), 2297–2340. https://doi.org/10.1002/sim.6128

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1. Causal research designs and analysis in education;Asia Pacific Education Review;2024-07-26

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