mlsanityData to 7 Oct 2026

TabCF: Distributional Control Function Estimation with Tabular Foundation Models

arXiv 2605.05993 ↗First posted 7 May 2026stat.ML cs.LG stat.MEcited by 0

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Abstract (arXiv)

Instrumental variable (IV) and control function (CF) methods are powerful tools for causal effect estimation in the presence of unmeasured confounding, yet most existing approaches target only mean effects and/or demand substantial fitting and tuning effort. In this paper, we introduce a simple method, TabCF, for control function regression using tabular foundation models, which enables accurate, fast, identification-transparent, and tuning-light causal estimation of distributional quantities, such as interventional means and quantiles; we also propose a copula-based approximation for multivariate outcomes. TabCF performs favorably against representative methods across a broad range of small- to medium-sized synthetic and real data scenarios. The central message is two-fold: for practitioners, it highlights that TabCF is an effective tool for distributional causal inference; for researchers, it suggests that the proposed approach could be considered a strong baseline for future method development. Code is available at https://github.com/GepingChen/TabCF.

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References · 22

  1. 2022TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Secondcited by 643
  2. 2021Transformers Can Do Bayesian Inferencecited by 338
  3. 2025TabICL: A Tabular Foundation Model for In-Context Learning on Large Datacited by 267
  4. 2025TabArena: A Living Benchmark for Machine Learning on Tabular Datacited by 152
  5. 2025TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Modelscited by 115
  6. 2026TabICLv2: A better, faster, scalable, and open tabular foundation modelcited by 112
  7. 2024TabDPT: Scaling Tabular Foundation Models on Real Datacited by 97
  8. 2019Deep Generalized Method of Moments for Instrumental Variable Analysiscited by 88
  9. 2025Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Modelscited by 67
  10. 2023Statistical Foundations of Prior-Data Fitted Networkscited by 59
  11. 2024Retrieval & Fine-Tuning for In-Context Tabular Modelscited by 58
  12. 2025Do-PFN: In-Context Learning for Causal Effect Estimationcited by 46
10 more
  1. 2025CausalPFN: Amortized Causal Effect Estimation via In-Context Learningcited by 43
  2. 2019A simple measure of conditional dependencecited by 38
  3. 2025Foundation Models for Causal Inference via Prior-Data Fitted Networkscited by 30
  4. 2020Valid Causal Inference with (Some) Invalid Instrumentscited by 19
  5. 2022Exploiting Independent Instruments: Identification and Distribution Generalizationcited by 18
  6. 2025Black Box Causal Inference: Effect Estimation via Meta Predictioncited by 13
  7. 2016Control Function Instrumental Variable Estimation of Nonlinear Causal Effect Modelscited by 7
  8. 2023Discovery and inference of a causal network with hidden confoundingcited by 7
  9. 2024Instrumental Variable Estimation of Distributional Causal Effectscited by 3
  10. 2025Distributional Instrumental Variable Methodcited by 2

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