Attenuated Heterogeneity in Fixed-Effects Causal Forests, and a Cross-Fitted Correction

• URL: http://arxiv.org/abs/2607.22896v1
• Date: Fri, 24 Jul 2026 20:18:11 GMT
• Title: Attenuated Heterogeneity in Fixed-Effects Causal Forests, and a Cross-Fitted Correction
• Abstract summary: Causal forests estimate conditional average treatment effects by averaging honest leaf-level effects across trees.<n>We show that this averaging systematically attenuates the estimated heterogeneity.<n>We adapt the best-linear-predictor calibration of Chernozhukov et al., estimating the de-attenuation slope out-of-bag.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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