Hybridizing Machine Learning Methods and Finite Mixture Models for Estimating Heterogeneous Treatment Effects in Latent Classes
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DOI: 10.3102/1076998620951983
Abstract: 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… read more here.
Keywords: finite mixture; treatment effects; effects latent; latent classes ... See more keywords