用单一模型快速推断不同数据集的因果机制，支持新数据生成和分布外泛化。
Amortized Inference of Causal Models via Conditional Fixed-Point Iterations
- 通过变压器架构学习数据嵌入，条件化推断因果机制。
- 在少样本场景下性能优于逐数据集训练的基线方法。
- 无需参数更新即可生成观测与干预数据，适合快速原型设计。
结构因果模型（SCMs）为干预推理和分布外泛化提供了严谨框架，是科学发现的关键目标。然而，从观测数据中学习SCMs面临巨大挑战，通常需为每个数据集单独训练模型。本文提出一种摊销推断框架，仅用一个模型即可根据观测数据和因果图预测因果机制。首先采用基于Transformer的架构学习数据集嵌入，再将固定点法（FiP）扩展为条件化推断机制。作为副产品，该方法可在推理时生成新SCM的观测与干预数据，无需参数更新。实验表明，该方法在分布内与分布外任务上均达到与逐数据集训练基线相当的性能，且在数据稀缺场景下表现更优。
原文摘要 · Abstract (English)
Structural Causal Models (SCMs) offer a principled framework to reason about interventions and support out-of-distribution generalization, which are key goals in scientific discovery. However, the task of learning SCMs from observed data poses formidable challenges, and often requires training a separate model for each dataset. In this work, we propose an amortized inference framework that trains a single model to predict the causal mechanisms of SCMs conditioned on their observational data and causal graph. We first use a transformer-based architecture for amortized learning of dataset embeddings, and then extend the Fixed-Point Approach (FiP) to infer the causal mechanisms conditionally on their dataset embeddings. As a byproduct, our method can generate observational and interventional data from novel SCMs at inference time, without updating parameters. Empirical results show that our amortized procedure performs on par with baselines trained specifically for each dataset on both in and out-of-distribution problems, and also outperforms them in scarce data regimes.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。