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.

中文翻译：

---

使用因果森林评估成本效益分析中的异质性

我们开发了一种数据驱动的估计和分析成本效益分析 (CEA) 中的异质性的方法，该方法使用实验或观察的个人水平数据。我们的实现使用因果森林和交叉拟合增强逆概率加权学习来估计增量结果、成本和净货币收益以及与 CEA 相关的其他参数的异质性。我们还展示了如何以相关方式可视化结果以分析 CEA 中的异质性，例如使用个人级别的成本效益平面。使用模拟数据集和实现我们方法的 R 包，我们展示了如何使用该方法来估计整个样本或亚群的平均成本效益，探索和分析增量结果的异质性，