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Denoised IPW-Lasso for Heterogeneous Treatment Effect Estimation in Randomized Experiments
Mingqian Guan, Komei Fujita, Naoya Sueishi, Shota Yasui
https://arxiv.org/abs/2510.10527

Denoised IPW-Lasso for Heterogeneous Treatment Effect Estimation in Randomized Experiments
This paper proposes a new method for estimating conditional average treatment effects (CATE) in randomized experiments. We adopt inverse probability weighting (IPW) for identification; however, IPW-transformed outcomes are known to be noisy, even when true propensity scores are used. To address this issue, we introduce a noise reduction procedure and estimate a linear CATE model using Lasso, achieving both accuracy and interpretability. We theoretically show that denoising reduces the predictio…

Revised comment on the paper titled "The Origin of Quantum Mechanical Statistics: Insights from Research on Human Language
Miko{\l}aj Sienicki, Krzysztof Sienicki
https://arxiv.org/abs/2512.07881 https://arxiv.org/pdf/2512.07881 https://arxiv.org/html/2512.07881
arXiv:2512.07881v1 Announce Type: new
Abstract: This short note comments on \citet{Aerts2024Origin}, which proposes that ranked word frequencies in texts should be read through the lens of Bose--Einstein (BE) statistics and even used to illuminate the origin of quantum statistics in physics. The core message here is modest: the paper offers an interesting analogy and an eye-catching fit, but several key steps mix physical claims with definitions and curve-fitting choices. We highlight three such points: (i) a normalization issue that is presented as "bosonic enhancement", (ii) an identification of rank with energy that makes the BE fit only weakly diagnostic of an underlying mechanism, and (iii) a baseline comparison that is too weak to support an ontological conclusion. We also briefly flag a few additional concerns (interpretation drift, parameter semantics, and reproducibility).
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