用表格基础模型快速精准估计因果分布效应
TabCF: Distributional Control Function Estimation with Tabular Foundation Models
- 基于表格基础模型构建控制函数回归，无需复杂调参
- 可准确估计干预均值与分位数等分布性因果效应
- 适合需要透明、高效因果推断的研究者和从业者
工具变量（IV）和控制函数（CF）方法在存在未观测混杂时是强大的因果效应估计工具，但现有方法大多仅关注均值效应，且需大量拟合与调参。本文提出一种简单方法TabCF，利用表格基础模型进行控制函数回归，实现对分布量（如干预均值与分位数）的高精度、快速、识别透明且调参极少的因果估计；同时提出基于拷贝的多变量结果近似方法。TabCF在多种中小型合成与真实数据场景中表现优于代表性方法。核心启示：对实践者而言，TabCF是分布因果推断的有效工具；对研究者而言，该方法可作为未来方法开发的强基线。代码已开源：https://github.com/GepingChen/TabCF。
原文摘要 · Abstract (English)
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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