Sverdrup Erik, Petukhova Maria, Wager Stefan
Department of Econometrics & Business Statistics, Monash University, Melbourne, Australia.
Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA.
Int J Methods Psychiatr Res. 2025 Jun;34(2):e70015. doi: 10.1002/mpr.70015.
Flexible machine learning tools are increasingly used to estimate heterogeneous treatment effects.
This paper gives an accessible tutorial demonstrating the use of the causal forest algorithm, available in the R package grf.
We start with a brief non-technical overview of treatment effect estimation methods, focusing on estimation in observational studies; the same techniques can also be applied in experimental studies. We then discuss the logic of estimating heterogeneous effects using the extension of the random forest algorithm implemented in grf. Finally, we illustrate causal forest by conducting a secondary analysis on the extent to which individual differences in resilience to high combat stress can be measured among US Army soldiers deploying to Afghanistan based on information about these soldiers available prior to deployment. We illustrate simple and interpretable exercises for model selection and evaluation, including targeting operator characteristics curves, Qini curves, area-under-the-curve summaries, and best linear projections.
A replication script with simulated data is available at https://github.com/grf-labs/grf/tree/master/experiments/ijmpr.
灵活的机器学习工具越来越多地用于估计异质性治疗效果。
本文提供了一个易于理解的教程，展示了R包grf中可用的因果森林算法的使用方法。
我们首先对治疗效果估计方法进行简要的非技术性概述，重点是观察性研究中的估计；同样的技术也可以应用于实验性研究。然后，我们讨论使用grf中实现的随机森林算法扩展来估计异质性效果的逻辑。最后，我们通过对部署到阿富汗的美国陆军士兵中，根据部署前可得的这些士兵的信息来测量个体对高战斗压力的恢复力差异程度进行二次分析，来说明因果森林。我们展示了用于模型选择和评估的简单且可解释的练习，包括目标算子特征曲线、基尼曲线、曲线下面积总结和最佳线性投影。
可在https://github.com/grf-labs/grf/tree/master/experiments/ijmpr获取带有模拟数据的复制脚本。
