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Learning to Fluctuate: Statistical Foundations for Causal Tabular Pretraining — arXiv:2609.26290

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.26290
License (as archived): CC0-1.0
Archived: 2026-10-08T15:08:53.856Z
SHA-256 of the archived text: 540a9e21388e9dd9d8b1d16c008dd30f4c7541f99729beec1e42f4a546cd5bc8
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How to cite

Learning to Fluctuate: Statistical Foundations for Causal Tabular Pretraining — arXiv:2609.26290 — arXiv (en), https://arxiv.org/abs/2609.26290 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f7535540539e (Metraipolis archive dc_f7535540539e, sha256 540a9e21388e9dd9d8b1d16c008dd30f4c7541f99729beec1e42f4a546cd5bc8)

BibTeX

@misc{dcf7535540539e,
  title = {Learning to Fluctuate: Statistical Foundations for Causal Tabular Pretraining — arXiv:2609.26290},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.26290},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f7535540539e; sha256 540a9e21388e9dd9d8b1d16c008dd30f4c7541f99729beec1e42f4a546cd5bc8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.26290. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Causal tabular foundation models amortize effect estimation across synthetic mechanisms, but latent-effect supervision rewards posterior shrinkage rather than encoding the repeated-sample response needed in a fixed deployment population. We introduce fluctuation-supervised pretraining (FSP): each synthetic table is labeled by its average treatment effect plus its efficient influence-function fluctuation; deployment remains a frozen forward pass. Along the path $T_{\lambda,P}=\theta(P)+\lambda P_n\psi_P$, we prove an endpoint transition: every fixed $\lambda<1$ retains label ambiguity of order $(1-\lambda)^2/n$, whereas full fluctuation makes the Gaussian label observable and reduces optimal finite-stratum causal label-prediction risk to order $n^{-2}$. A finite-pretraining bound combines label, network, episode-sampling, and optimization errors; its sampling defect controls fixed-mechanism bias, mean squared error, variance, Gaussian approximation, and, with variance-head accuracy, studentized coverage. Complementary lower bounds separate local $n^{-1}$ ATE risk from the $\log N/M$ excess risk of generic finite-dictionary episode learning. Experiments trace the learned sampling response. Across 24 nonlinear continuous-covariate cells at trained context lengths, continuous-row FSP lowers checkpoint-mean macro RMSE by 7.0% versus S-learner and wins all 12 weak-overlap cells; validation-selected Summary FSP deploys $11.6\times$ faster per table in our warm one-thread benchmark. Under effect shift, matched Raw FSP lowers mean-checkpoint RMSE by 54.2% and teacher defect by 99.0% versus latent-effect supervision, and RMSE by 10.2% versus the released CausalPFN-S checkpoint. Known-effect semisynthesis tests coverage; two randomized-study evaluations show that lower RMSE can coexist with residual attenuation.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-23 in cs.LG).
Categorie arXiv: Machine Learning (stat.ML) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.26290 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T14:37:29.564Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-23?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).