Jointly Learning Consistent Causal Abstractions Over Multiple Interventional Distributions
This work addresses the challenge of integrating causal models at varying resolutions for researchers in causal inference, though it appears incremental as it builds on existing formalizations.
The paper tackles the problem of learning causal abstractions that are consistent across multiple interventional distributions, enabling joint reasoning at different granularities while preserving cause-effect relationships. It introduces a first framework based on Rischel (2020) and a differentiable programming solution, demonstrating performance benefits on synthetic and real-world electric vehicle battery manufacturing data.
An abstraction can be used to relate two structural causal models representing the same system at different levels of resolution. Learning abstractions which guarantee consistency with respect to interventional distributions would allow one to jointly reason about evidence across multiple levels of granularity while respecting the underlying cause-effect relationships. In this paper, we introduce a first framework for causal abstraction learning between SCMs based on the formalization of abstraction recently proposed by Rischel (2020). Based on that, we propose a differentiable programming solution that jointly solves a number of combinatorial sub-problems, and we study its performance and benefits against independent and sequential approaches on synthetic settings and on a challenging real-world problem related to electric vehicle battery manufacturing.