Bias-adjusted three-step latent class analysis (LCA) is widely popular to relate covariates to class membership. However, if the causal effect of a treatment on class membership is of interest and only observational data is available, causal inference techniques such as inverse propensity weighting (IPW) need to be used. In this article, we extend the bias-adjusted three-step LCA to incorporate IPW. This approach separates the estimation of the measurement model from the estimation of the treatment effect using IPW only for the later step. Compared to previous methods, this solves several conceptual issues and more easily facilitates model selection and the use of multiple imputation. This new approach, implemented in the software Latent GOLD, is evaluated in a simulation study and its use is illustrated using data of prostate cancer patients.

偏差调整的三步潜在类分析 (LCA) 广泛流行用于将协变量与类成员相关联。但是，如果处理对类成员的因果影响感兴趣并且只有观察数据可用，则需要使用因果推断技术，例如逆倾向加权 (IPW)。在本文中，我们扩展了偏置调整的三步 LCA 以包含 IPW。这种方法将测量模型的估计与仅在后面的步骤中使用 IPW 的治疗效果估计分开。与以前的方法相比，这解决了几个概念问题，并且更容易促进模型选择和多重插补的使用。这种在软件 Latent GOLD 中实施的新方法在模拟研究中进行了评估，并使用前列腺癌患者的数据进行了说明。