A two-stage meta-analysis with parametric or machine-learning first-stage models produces 95% prediction intervals for conditional average treatment effects in a target patient population.
On the Distinction Between "Conditional Average Treatment Effects" (CATE) and "Individual Treatment Effects" (ITE) Under Ignorability Assumptions
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abstract
Recent years have seen a swell in methods that focus on estimating "individual treatment effects". These methods are often focused on the estimation of heterogeneous treatment effects under ignorability assumptions. This paper hopes to draw attention to the fact that there is nothing necessarily "individual" about such effects under ignorability assumptions and isolating individual effects may require additional assumptions. Such individual effects, more often than not, are more precisely described as "conditional average treatment effects" and confusion between the two has the potential to hinder advances in personalized and individualized effect estimation.
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Precision Mental Health: Predicting Heterogeneous Treatment Effects for Depression through Data Integration
A two-stage meta-analysis with parametric or machine-learning first-stage models produces 95% prediction intervals for conditional average treatment effects in a target patient population.