CIDER-FM：面向多样实验机制因果推断的基础模型
CIDER-FM: Foundation Models for Causal Inference from Diverse Experimental Regimes
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中文总结 AI 辅助
本研究提出CIDER-FM，一种结合观测与替代干预数据、利用干预感知表示和三轴注意力机制预测条件干预分布的因果基础模型，实验证明其优于仅用观测数据的方法。
中文摘要 AI 辅助
因果基础模型（CFM）通过在合成结构因果模型（SCM）的先验上进行摊销化因果推断，预测实验对特定变量的影响。然而，仅凭观测数据可能使多种因果模型与现有证据兼容，而对感兴趣变量进行精确干预的实验数据可能无法获得。本研究将CFM作为一种结合有限观测数据集和替代干预数据集的方法，以比仅使用观测数据更准确地预测目标条件干预分布（CID）。我们首先形式化了替代实验的概念性益处。在此分析基础上，我们引入了“面向多样实验机制因果推断的基础模型”（CIDER-FM），这是一种因果基础模型，利用干预感知表示和分层三轴注意力机制，在变量、样本和实验机制之间交换信息。我们在多样的合成图与机制族上，以及在来自Causal Chambers的模拟和真实世界数据上，将CIDER-FM与广泛的基线方法进行了评估。我们的结果展示了强大的CID预测性能，并表明纳入实验上下文可以比仅使用观测数据提高预测能力。
英文摘要
Causal foundation models (CFMs) amortise causal inference over priors of synthetic structural causal models (SCMs), predicting the effect of an experiment on a specific variable. However, observational data alone may leave multiple causal models compatible with available evidence, while experimental data with interventions on exactly the variable of interest might be unavailable. This work studies CFMs as a method to combine finite observational and surrogate-interventional datasets in order to predict a target conditional interventional distribution (CID) more accurately than with observational data alone. We first formalise the conceptual benefits of surrogate experiments. Building on this analysis, we introduce \textsc{Foundation Models for Causal Inference from Diverse Experimental Regimes} (\emph{CIDER-FM}), a causal foundation model that uses an intervention-aware representation and hierarchical three-axis attention to exchange information across variables, samples, and experimental regimes. We evaluate CIDER-FM against a wide range of baselines across diverse synthetic graph and mechanism families, as well as on both simulated and real-world data from Causal Chambers. Our results demonstrate strong CID prediction performance and show that incorporating experimental context can improve predictions over observational data alone.
发表机构
- University of Cambridge（剑桥大学）
- Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
- Prior Labs
- Gatsby Computational Neuroscience Unit, University College London（伦敦大学学院盖茨比计算神经科学单元）
- ELLIS Institute（ELLIS研究所）
- The Alan Turing Institute（艾伦·图灵研究所）
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