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

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  • Harry Aytug

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

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  • Harry Aytug, 2026. "Attenuated Heterogeneity in Fixed-Effects Causal Forests, and a Cross-Fitted Correction," Papers 2607.22896, arXiv.org.
    • Handle: RePEc:arx:papers:2607.22896
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    References listed on IDEAS

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    1. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    2. Yan Leng & Drew Dimmery, 2024. "Calibration of Heterogeneous Treatment Effects in Randomized Experiments," Information Systems Research, INFORMS, vol. 35(4), pages 1721-1742, December.
    3. Evelina Gavrilova & Audun Langørgen & Floris T. Zoutman, 2025. "Difference‐in‐Difference Causal Forests With an Application to Payroll Tax Incidence in Norway," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(7), pages 727-740, November.
    4. Callaway, Brantly & Sant’Anna, Pedro H.C., 2021. "Difference-in-Differences with multiple time periods," Journal of Econometrics, Elsevier, vol. 225(2), pages 200-230.
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