This episode focuses on methods for estimating and validating individualized treatment effects, particularly using machine learning (ML) techniques. It explores various "meta-learning" strategies like the S-Learner, T-Learner, Doubly Robust (DR)-Learner, and Residual (R)-Learner, comparing their strengths and weaknesses in different data scenarios. The text also discusses covariate shift and its implications for model performance, proposing adjustments. Finally, it addresses model selection and ensembling for CATE models, along with crucial validation techniques such as heterogeneity tests, calibration checks, and uplift curves to assess model quality and interpret treatment effects.
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