ProximalFM：隐藏混杂下的摊销式近端因果推断

中文摘要 AI 辅助

标准因果识别方法通常假设不存在未测量的混杂，当相关混杂因子未被观测时，这些方法可能会失效。近端因果推断转而利用代理变量在隐藏混杂下识别效应。然而，非参数近端估计在实践中可能具有挑战性：恢复诸如条件平均处理效应（CATE）之类的因果估计量需要求解一个不适定的积分方程，该方程对数据需求大、对超参数敏感且优化不稳定。对此类模型进行贝叶斯推断提供了一种理想的替代方案，通过先验正则化来缓解这些困难。然而，计算后验本身也具有挑战性，因为典型的似然函数会包含潜在变量。借鉴表格基础模型在后门、工具变量和前门设置中的近期成功，我们提出先验数据拟合网络（PFNs）特别适合解决这一瓶颈。实际上，通过在与合规结构因果模型对应的合成数据上进行训练，并访问神谕反事实，我们大幅简化了任务，将隐含的贝叶斯算子反演摊销为单次Transformer前向传播。与先前主要关注点估计的文献相比，我们的模型ProximalFM明确针对CATE的贝叶斯后验分布。该问题的一个独特方面在于，我们需要提供神谕CATE的蒙特卡洛估计，这导致了一种新的PFN变体，该变体考虑了附加的随机误差。在多种近端机制的综合测试中，ProximalFM无需针对数据集进行调参即可实现持续强劲的CATE估计性能，其最大优势出现在潜在混杂显著且代理信息较弱的情况下；它还通过单次摊销前向传播提供快速推断。

英文摘要

Standard causal identification methods often assume no unmeasured confounding and can fail when relevant confounders are unobserved. Proximal causal inference instead uses proxy variables to identify effects under hidden confounding. However, nonparametric proximal estimation can be challenging in practice: recovering causal estimands such as the conditional average treatment effect (CATE) requires solving an ill-posed integral equation that is data-hungry, hyperparameter-sensitive, and optimization-unstable. Bayesian inference for such models provides a desirable alternative, mitigating these difficulties by regularizing through the prior. However, computing a posterior is itself challenging, as a typical likelihood function will include latent variables. Following the recent success of tabular foundation models in backdoor, instrumental variable, and frontdoor settings, we propose that prior-data fitted networks (PFNs) are uniquely suited to resolve this bottleneck. Indeed, by training on synthetic data sampled from compliant structural causal models with access to oracle counterfactuals, we simplify the task substantially, amortizing the implied Bayesian operator inversion into a single transformer forward pass. Compared to prior literature that focuses primarily on point estimation, our model, ProximalFM, explicitly targets the Bayesian posterior distribution of the CATE. One unique aspect of this problem is that we need to provide Monte Carlo estimates of the oracle CATEs, leading to a novel variation of PFNs that accounts for the added stochastic error. Across a diverse suite of proximal regimes, ProximalFM achieves consistently strong CATE-estimation performance without dataset-specific tuning, with its largest advantage when latent confounding is substantial and the proxies are weakly informative; it also provides fast inference through a single amortized forward pass.

发表机构

• University of Oxford（牛津大学）
• Harvard University（哈佛大学）
• University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
• Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）
• Vector Institute（向量研究所）
• University College London（伦敦大学学院）

机构由 AI 辅助整理，请以论文原文为准。