What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?
森林型异质性处理效应估计为何有效？AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要围绕随机试验和观察研究中的异质性处理效应估计，并分析混杂调整的作用，直接对应因果推断。
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森林型异质性处理效应估计为何有效？
- 研究问题
- 因果森林与基于模型的森林各依赖哪些计算环节，它们在随机试验和存在混杂的观察数据中为何表现不同？
- 方法与思路
- 作者在平方损失的加性模型下统一两类森林，构造混合版本，并通过模拟基准比较处理与结果局部中心化及分裂选择。
- 材料陈述的贡献
- 摘要称，随机试验中两类方法表现相近；存在混杂时，按倾向得分局部中心化处理变量是良好表现的主要驱动因素。
- 阅读关注点
- 应核对统一模型的参数与假设、各计算环节的独立比较方式，以及倾向得分估计误差和混杂强度如何影响基准结果。
- 阅读边界
- 所据为 arXiv 预印本，同行评审及期刊接收未经核实；摘要未说明全部模拟设定，不能据此认定某一环节普遍最优。
原文摘要
Estimation of heterogeneous treatment effects (HTE) is of prime importance in many disciplines, ranging from personalized medicine to economics among many others. Random forests have been shown to be a flexible and powerful approach to HTE estimation in both randomized trials and observational studies. In particular "causal forests", introduced by Athey, Tibshirani and Wager (2019), along with the R implementation in package grf were rapidly adopted. A related approach, called "model-based forests", that is geared towards randomized trials and simultaneously captures effects of both prognostic and predictive variables, was introduced by Seibold, Zeileis and Hothorn (2018) along with a modular implementation in the R package model4you. Here, we present a unifying view that goes beyond the theoretical motivations and investigates which computational elements make causal forests so successful and how these can be blended with the strengths of model-based forests. To do so, we show that both methods can be understood in terms of the same parameters and model assumptions for an additive model under L2 loss. This theoretical insight allows us to implement several flavors of "model-based causal forests" and dissect their different elements in silico. The original causal forests and model-based forests are compared with the new blended versions in a benchmark study exploring both randomized trials and observational settings. In the randomized setting, both approaches performed akin. If confounding was present in the data generating process, we found local centering of the treatment indicator with the corresponding propensities to be the main driver for good performance. Local centering of the outcome was less important, and might be replaced or enhanced by simultaneous split selection with respect to both prognostic and predictive effects.