Structural Causal Bottleneck Models
This provides a flexible framework for task-specific dimension reduction in causal inference, offering an alternative to existing methods like causal representation learning.
The paper tackles the problem of causal effect estimation in high-dimensional variables by introducing structural causal bottleneck models (SCBMs), which assume effects depend on low-dimensional bottlenecks, and demonstrates benefits for effect estimation in low-sample transfer learning settings.
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.