There has been increasing interest in exploring heterogeneous treatment effects using machine learning (ML) methods such as causal forests, Bayesian additive regression trees, and targeted maximum likelihood estimation. However, there is little work on applying these methods to estimate treatment effects in latent classes defined by well-established finite mixture/latent class models. This article proposes a hybrid method, a combination of finite mixture modeling and ML methods from causal inference to discover effect heterogeneity in latent classes. Our simulation study reveals that hybrid ML methods produced more precise and accurate estimates of treatment effects in latent classes. We also use hybrid ML methods to estimate the differential effects of private lessons across latent classes from Trends in International Mathematics and Science Study data.

人们对使用机器学习（ML）方法（例如因果森林，贝叶斯加性回归树和目标最大似然估计）探索异构处理效果的兴趣日益浓厚。但是，在将这些方法用于评估由完善的有限混合/潜在类模型定义的潜在类中的治疗效果方面所做的工作很少。本文提出了一种混合方法，将有限混合模型与基于因果推理的ML方法相结合，以发现潜在类中的效果异质性。我们的模拟研究表明，混合ML方法对潜在类别中的治疗效果产生了更精确的估计。