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Representation Multiplicity in Causal Forests

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  • Yi Niu

Abstract

Including covariates alongside strictly monotone transformations can change a causal forest's treatment decisions without adding information. Random feature selection favors covariates represented by these multiple columns. Under stated conditions, I show that this imbalance can persist as samples grow. Treatment effect components associated with other covariates are omitted, attenuated, or recovered depending on their inclusion probabilities and tree depth. Simulations and a job-training replication illustrate sensitivity to redundant transformations. Sampling groups of variables that generate identical splits restores prediction invariance on the grouping data when fitting and randomization are held fixed.

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  • Yi Niu, 2026. "Representation Multiplicity in Causal Forests," Papers 2609.12406, arXiv.org, revised Oct 2026.
    • Handle: RePEc:arx:papers:2609.12406
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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. Jason M. Klusowski & Peter M. Tian, 2024. "Large Scale Prediction with Decision Trees," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(545), pages 525-537, January.
    3. Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2022. "Automatic Debiased Machine Learning of Causal and Structural Effects," Econometrica, Econometric Society, vol. 90(3), pages 967-1027, May.
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