Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship
This work addresses the challenge of scalable causal discovery for researchers and practitioners, offering a more efficient method that is incremental in nature.
The paper tackles the problem of recovering causal graphs from observational data by introducing a framework that tests for distributional invariance of cause-effect relationships, achieving up to 25x faster processing than state-of-the-art methods while maintaining or improving performance on large-scale benchmarks.
This paper introduces a new framework for recovering causal graphs from observational data, leveraging the observation that the distribution of an effect, conditioned on its causes, remains invariant to changes in the prior distribution of those causes. This insight enables a direct test for potential causal relationships by checking the variance of their corresponding effect-cause conditional distributions across multiple downsampled subsets of the data. These subsets are selected to reflect different prior cause distributions, while preserving the effect-cause conditional relationships. Using this invariance test and exploiting an (empirical) sparsity of most causal graphs, we develop an algorithm that efficiently uncovers causal relationships with quadratic complexity in the number of observational variables, reducing the processing time by up to 25x compared to state-of-the-art methods. Our empirical experiments on a varied benchmark of large-scale datasets show superior or equivalent performance compared to existing works, while achieving enhanced scalability.