Estimating Average Causal Effect in Latent Class Analysis
잠재범주분석을 이용한 원인적 영향력 추론에 관한 연구

Gayoung Park, Hwan Chung
2014 Korean Journal of Applied Statistics  
Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. Recently, new methods for the causal inference in the observational studies have been proposed such as the matching with the propensity score or the inverse probability treatment weighting. They have focused on how to control the confounders and how to evaluate the effect of the treatment on the result variable. However, these conventional methods are valid
more » ... ly when the treatment variable is categorical and both of the treatment and the result variables are directly observable. Research on the causal inference can be challenging in part because it may not be possible to directly observe the treatment and/or the result variable. To address this difficulty, we propose a method for estimating the average causal effect when both of the treatment and the result variables are latent. The latent class analysis has been applied to calculate the propensity score for the latent treatment variable in order to estimate the causal effect on the latent result variable. In this work, we investigate the causal effect of adolescents delinquency on their substance use using data from the 'National Longitudinal Study of Adolescent Health.'
doi:10.5351/kjas.2014.27.7.1077 fatcat:g74iaqx7e5fs5eqtbjjzabiq6e