AI 中文总结
研究固定效应面板设置中因果森林估计条件平均治疗效果时异质性衰减问题，刻画其随设计变化情况，提出用Chernozhukov等人的校准方法补救，模拟和实际应用中均有效果，方法集成到Python包。
AI 中文摘要
通过对诚实叶级效应进行平均来估计条件平均治疗效果的因果森林，在固定效应面板设置中被广泛使用。我们发现这种平均会系统性地衰减估计的异质性：原始预测表现为a + b*tau(x)，斜率b < 1，因此CATEs的分布被压缩向平均效应，用于报告无偏平均治疗效果的加法重新中心化并不能解决这个问题。通过与基于相同变换信号的相似性加权广义随机森林进行基准测试，我们发现两种估计器都会衰减，但叶平均构造的衰减更严重。我们刻画了b如何随设计变化，在低信噪比、较小面板和高维度时恶化，这一诊断是我们的主要贡献。作为补救措施，我们采用Chernozhukov等人的最佳线性预测器校准方法，通过袋外估计去衰减斜率，使其在观测面板内自包含，并且在同质效应下渐近惰性。在模拟中，相对于重新中心化默认方法，该校正将CATE均方误差降低了25 - 42%；在标准县最低工资面板上，衰减存在但较轻，校正恢复了施加的分布。我们将该方法集成到causalfe Python包中。
英文摘要
Causal forests that estimate conditional average treatment effects by averaging honest leaf-level effects across trees are widely used in fixed-effects panel settings. We show that this averaging systematically attenuates the estimated heterogeneity: the raw prediction behaves like a + b*tau(x) with slope b < 1, so the spread of the CATEs is compressed toward the average effect, and the additive recentering used to report an unbiased average treatment effect does not fix it. Benchmarking against a similarity-weight generalized random forest on the same within-transformed signal, we find both estimators attenuate but the leaf-averaging construction attenuates materially more. We characterize how b moves with the design, worsening with lower signal-to-noise, smaller panels, and higher dimension; this diagnosis is our main contribution. As a remedy we adapt the best-linear-predictor calibration of Chernozhukov et al., estimating the de-attenuation slope out-of-bag so that it is self-contained within the observational panel and asymptotically inert under a homogeneous effect. In simulations the correction cuts CATE mean-squared error by 25-42% relative to the recentering default; on a standard county minimum-wage panel the attenuation is present but mild and the correction restores the imposed spread. We ship the method in the causalfe Python package.
Comments20 pages, 3 tables
