用低维瓶颈总结高维变量因果关系，提升小样本迁移学习效果
Structural Causal Bottleneck Models
- 假设高维变量的因果效应仅依赖于低维瓶颈统计量
- 在小样本迁移学习中，瓶颈结构使因果效应估计更准确
- 方法简单可训练，适合任务特定降维场景
我们提出结构化因果瓶颈模型（SCBMs），一种新型结构化因果模型。其核心假设是：高维变量间的因果效应仅依赖于原因的低维摘要统计量（即瓶颈）。SCBMs 提供了一种灵活的任务特定降维框架，且可通过标准简单学习算法实际估计。我们分析了 SCBMs 的可识别性，将其与 Tishby & Zaslavsky (2015) 的信息瓶颈理论相联系，并演示了实验估计方法。还证明了在小样本迁移学习设置中，瓶颈结构能显著提升因果效应估计性能。我们认为 SCBMs 是现有因果降维方法（如因果表征学习或因果抽象学习）的可行替代方案。
原文摘要 · Abstract (English)
We introduce structural causal bottleneck models (SCBMs), a novel class of structural causal models. At the core of SCBMs lies the assumption that causal effects between high-dimensional variables only depend on low-dimensional summary statistics, or bottlenecks, of the causes. SCBMs provide a flexible framework for task-specific dimension reduction while being estimable via standard, simple learning algorithms in practice. We analyse identifiability in SCBMs, connect them to information bottlenecks in the sense of Tishby & Zaslavsky (2015), and illustrate how to estimate them experimentally. We also demonstrate the benefit of bottlenecks for effect estimation in low-sample transfer learning settings. We argue that SCBMs provide an alternative to existing causal dimension reduction frameworks like causal representation learning or causal abstraction learning.
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