Using causal forests to assess heterogeneity in cost‐effectiveness analysis
开发了一种基于因果森林的数据驱动方法，用于分析实验或观察数据中成本效果分析的异质性，可估计增量结果、成本和净货币收益的异质性，并可视化结果。
We develop a method for data-driven estimation and analysis of heterogeneity in cost-effectiveness analyses (CEA) with experimental or observational individual-level data. Our implementation uses causal forests and cross-fitted augmented inverse probability weighted learning to estimate heterogeneity in incremental outcomes, costs and net monetary benefits, as well as other parameters relevant to CEA. We also show how the results can be visualized in relevant ways for the analysis of heterogeneity in CEA, such as using individual-level cost effectiveness planes. Using a simulated dataset and an R package implementing our methods, we show how the approach can be used to estimate the average cost-effectiveness in the entire sample or in subpopulations, explore and analyze the heterogeneity in incremental outcomes, costs and net monetary benefits (and their determinants), and learn policy rules from the data.