Structural Causal Bottleneck Models
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 learn...
| Authors | Simon Bing, Jonas Wahl, Jakob Runge |
|---|---|
| Primary category | stat.ML |
| Published | 9 March 2026 |
| Last updated at source | 16 March 2026 |
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