Differentiable Invariant Causal Discovery
This addresses the challenge of causal discovery in machine learning for applications where data comes from varied environments, offering a more robust method, though it is incremental as it builds on existing differentiable frameworks.
The paper tackles the problem of learning causal structure from observational data, which is prone to biases, by proposing Differentiable Invariant Causal Discovery (DICD) that uses multi-environment information to avoid spurious edges and wrong directions, resulting in up to 36% improvement in SHD over state-of-the-art methods.
Learning causal structure from observational data is a fundamental challenge in machine learning. However, the majority of commonly used differentiable causal discovery methods are non-identifiable, turning this problem into a continuous optimization task prone to data biases. In many real-life situations, data is collected from different environments, in which the functional relations remain consistent across environments, while the distribution of additive noises may vary. This paper proposes Differentiable Invariant Causal Discovery (DICD), utilizing the multi-environment information based on a differentiable framework to avoid learning spurious edges and wrong causal directions. Specifically, DICD aims to discover the environment-invariant causation while removing the environment-dependent correlation. We further formulate the constraint that enforces the target structure equation model to maintain optimal across the environments. Theoretical guarantees for the identifiability of proposed DICD are provided under mild conditions with enough environments. Extensive experiments on synthetic and real-world datasets verify that DICD outperforms state-of-the-art causal discovery methods up to 36% in SHD. Our code will be open-sourced.