Dergiler / Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi / 2021 / Cilt: 12 - Sayı: 4

Covariate Balance as a Quality Indicator for Propensity Score Analysis

Sayfa
374–387
Yayın Tarihi
2022-10-12
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

It was also found that estimation of the treatment effect can be unbiased to some degree, even with a lack of balance under specific conditions.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Propensity score analysis, such as propensity score matching and propensity score weighting, is becoming increasingly popular in educational research.
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Temel Bulgular & Veri:Suitable covariate balance may still yield biased estimates of treatment effects.
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Sonuç & Katkı:It was also found that estimation of the treatment effect can be unbiased to some degree, even with a lack of balance under specific conditions.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Propensity score analysis · covariate balance · unbiased treatment effect

Abstract

Propensity score analysis, such as propensity score matching and propensity score weighting, is becoming increasingly popular in educational research. When a propensity score analysis is conducted, examining the covariate balance is considered to be crucial to justify the quality of the analysis results. However, it has been pointed out that solely considering how covariates balance after matching may not be enough for justifying the quality of the propensity score analysis results. Suitable covariate balance may still yield biased estimates of treatment effects. The current study aimed to systematically demonstrate this problem by a series of simulation studies. As a result, it was revealed that a good covariate balance on the mean and/or the variance does not guarantee reduced bias on an estimated treatment effect. It was also found that estimation of the treatment effect can be unbiased to some degree, even with a lack of balance under specific conditions.

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Bu Makaleye Atıf Yap

KARA, Y., KAMATA, A., POTGIETER, C. J., GALLEGOS, E., PATARAPICHAYATHAM, C. (2022). Covariate Balance as a Quality Indicator for Propensity Score Analysis. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, Cilt: 12 - Sayı: 4, 374–387. https://ajindex.com/articles/497165

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Kaynakça (11)

  1. 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
  2. Belitser, S. V., Martens, E. P., Pestman, W. R., Groenwold, R. H. H., Boer, A. de, & Klungel, E. H. (2011). Measuring balance and model selection in propensity score methods. Pharmacoepidemiology and Drug Safety, 20(11), 1115–1129. https://doi.org/10.1002/pds.2188
  3. Bhattacharya, J., & Vogt, W. B. (2007). Do instrumental variables belong in propensity scores? Cambridge, MA: National Bureau of Economic Research (NBER) Working Paper Series No. 343. Brookhart, M. A., Schneeweiss, S., Rothman, K. J., Glynn, R. J., Avorn, J., & Sturmer, T. (2006). Variable selection for propensity score models. American Journal of Epidemiology, 163(12), 1149–1156. https://doi.org/10.1093/aje/kwj149
  4. Cannas, M., & Arpino, B. (2019). A comparison of machine learning algorithms and covariate balance measures for propensity score matching and weighting. Biometrical Journal, 61(4), 1049–1072. https://doi.org/10.1002/bimj.201800132
  5. Fong, C., Ratkovic, M., & Imai, K. (2019). CBPS: Covariate balancing propensity score. Retrieved from https://CRAN.R-project.org/package=CBPS
  6. Greifer, N. (2019). Cobalt: Covariate balance tables and plots. Retrieved from https://CRAN.R-project.org/package=cobalt
  7. Guo, S., & Fraser, M. W. (2015). Propensity score analysis: Statistical methods and applications (ed. 2). Thousand Oaks, CA: Sage. Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161–1189. https://doi.org/10.1111/1468-0262.00442
  8. Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2008). MatchIt: Nonparametric preprocessing for parametric causal inference. Journal of Statistical Software, 42(8), 1–28. https://doi.org/10.18637/jss.v042.i08
  9. Hong, H., Aaby, D. A., Siddique, J., & Stuart, E. A. (2018). Propensity score-based estimators with multiple error-prone covariates. American Journal of Epidemiology, 188, 222–230. https://doi.org/10.1093/aje/kwy210
  10. Imai, K., & Ratkovic, M. (2014). Covariate balancing propensity score. Journal of the Royal Statistical Society, 76, 243–263.
  11. Kainz, K., Greifer, N., Givens, A., Swietek, K., Lombardi, B. M., Zietz, S., & Kohn, J. L. (2017). Improving causal inference: Recommendations for covariate selection and balance in propensity score