TabCF: Distributional Control Function Estimation with Tabular Foundation Models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913098570399744 |
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| author | Chen, Geping Li, Chunlin Yang, Tianzhong Zhu, Zhengyuan Zhou, Jing |
| author_facet | Chen, Geping Li, Chunlin Yang, Tianzhong Zhu, Zhengyuan Zhou, Jing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05993 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TabCF: Distributional Control Function Estimation with Tabular Foundation Models Chen, Geping Li, Chunlin Yang, Tianzhong Zhu, Zhengyuan Zhou, Jing Machine Learning Methodology Other Statistics 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. |
| title | TabCF: Distributional Control Function Estimation with Tabular Foundation Models |
| topic | Machine Learning Methodology Other Statistics |
| url | https://arxiv.org/abs/2605.05993 |