DOI QR코드

DOI QR Code

Latent causal inference using the propensity score from latent class regression model

잠재범주회귀모형의 성향점수를 이용한 잠재변수의 원인적 영향력 추론 연구

  • Lee, Misol (Department of Statistics, Korea University) ;
  • Chung, Hwan (Department of Statistics, Korea University)
  • 이미솔 (고려대학교 통계학과) ;
  • 정환 (고려대학교 통계학과)
  • Received : 2017.07.05
  • Accepted : 2017.08.23
  • Published : 2017.10.31

Abstract

Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. The matching with the propensity score is one of the most popular methods to control the confounders in order to evaluate the effect of the treatment on the outcome variable. Recently, new methods for the causal inference in latent class analysis (LCA) have been proposed to estimate the average causal effect (ACE) of the treatment on the latent discrete variable. They have focused on the application study for the real dataset to estimate the ACE in LCA. In practice, however, the true values of the ACE are not known, and it is difficult to evaluate the performance of the estimated the ACE. In this study, we propose a method to generate a synthetic data using the propensity score in the framework of LCA, where treatment and outcome variables are latent. We then propose a new method for estimating the ACE in LCA and evaluate its performance via simulation studies. Furthermore we present an empirical analysis based on data form the 'National Longitudinal Study of Adolescents Health,' where puberty as a latent treatment and substance use as a latent outcome variable.

무작위 통제시험에서와 달리, 관찰연구에서는 편향되지 않은 인과관계를 추론하기 위한 통계적 전략이 필요하다. 최근 잠재범주분석(latent class analysis; LCA)에서 처치의 평균인과효과(average causal effect; ACE)를 추정하기 위한 새로운 방법들이 제안되었으나 이러한 방법들은 실제 데이터를 분석하는 응용 연구에 초점이 맞춰있다. 따라서 ACE의 참값을 알 수 없어 추정 방법의 성능을 평가하는 데 한계가 있다. 본 연구에서는 Park과 Chung(2014)이 제안한 방법을 개선하여, 다항범주형 처치변수가 잠재변수인 상황에서 다항범주형 결과변수에 미치는 인과효과 추정방법을 제안하고 처치변수와 결과변수가 잠재변수 또는 관측변수를 포함하는 여러 상황에서 본 연구가 제안한 인과효과 추정방법의 성능을 모의실험연구를 통하여 평가하고자 한다. 더불어 'National Longitudinal Study of Adolescents Health'자료를 사용하여 미국 여성 청소년 성장과 약물사용에 대한 인과효과를 추론하고자 한다.

Keywords

References

  1. Clogg, C. C. and Goodman, L. A. (1984). Latent structure analysis of a set of multidimensional contingency tables, Journal of the American Statistical Association, 79, 762-771. https://doi.org/10.1080/01621459.1984.10477093
  2. Cohen, J. (1988). Statistical Power Analysis for the Behavior Science, Lawrance Eribaum Association.
  3. Dayton, C. M. and Macready, G. B. (1988). Concomitant-variable latent-class models, Journal of the American Statistical Association, 83, 173-178. https://doi.org/10.1080/01621459.1988.10478584
  4. Frolich, M. (2004). Programme evaluation with multiple treatments, Journal of Economic Surveys, 18, 181-224. https://doi.org/10.1111/j.0950-0804.2004.00001.x
  5. Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models, Biometrika, 61, 215-231. https://doi.org/10.1093/biomet/61.2.215
  6. Lanza, S. T., Coffman, D. L., and Xu, S. (2013). Causal inference in latent class analysis, Structural Equation Modeling: A Multidisciplinary Journal, 20, 361-383. https://doi.org/10.1080/10705511.2013.797816
  7. Lazarsfeld, P. and Henry, N. (1968). Latent Structure Analysis, Houghton, Mifflin, New York.
  8. McCaffrey, D. F., Griffn, B. A., Almirall, D., Slaughter, M. E., Ramchand, R., and Burgette, L. F. (2013). A tutorial on propensity score estimation for multiple treatments using generalized boosted models, Statistics in Medicine, 32, 3388-3414. https://doi.org/10.1002/sim.5753
  9. Park, G. and Chung, H. (2014). Estimating average causal effect in latent class analysis, Korean Journal of Applied Statistics, 27, 1077-1095. https://doi.org/10.5351/KJAS.2014.27.7.1077
  10. Robins, J. M., Hernan, M. A., and Brumback, B. (2000). Marginal structural models and causal inference in epidemiology, Epidemiology, 11, 550-560. https://doi.org/10.1097/00001648-200009000-00011
  11. Rosenbaum, P. R. (2002). Observational studies. In Observational Studies (pp. 1-17), Springer.
  12. Rosenbaum, P. R. and Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects, Biometrika, 70, 41-55. https://doi.org/10.1093/biomet/70.1.41
  13. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies, Journal of Educational Psychology, 66, 688. https://doi.org/10.1037/h0037350
  14. Rubin, D. B. (1976). Inference and missing data, Biometrika, 63, 581-592. https://doi.org/10.1093/biomet/63.3.581
  15. Rubin, D. B. (1977). Assignment to treatment group on the basis of a covariate, Journal of Educational and Behavioral Statistics, 2, 1-26. https://doi.org/10.3102/10769986002001001
  16. Rubin, D. B. (1978). Bayesian inference for causal effects: the role of randomization, The Annals of Statistics, 6, 34-58. https://doi.org/10.1214/aos/1176344064
  17. Udry, J. R. (2003). The National Longitudinal Study of Adolescent Health (Add Health), Wave I, 1994-1995.