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

arXiv: https://arxiv.org/abs/2607.22896 · HTML: https://mlsanity.com/paper/2607.22896/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 24 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [Estimating Treatment Effects with Causal Forests: An Application](https://mlsanity.com/paper/1902.07409/) (2019)
- [Causal isotonic calibration for heterogeneous treatment effects](https://mlsanity.com/paper/2302.14011/) (2023)
- [Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India](https://mlsanity.com/paper/1712.04802/) (2017)

## Abstract (arXiv)

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.