Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method
This work addresses causal inference, a foundational challenge in machine learning and statistics, with potential applications in domains like medicine and economics, though it appears incremental as it builds on existing nonlinear ICA methods.
The paper tackles the problem of distinguishing cause from effect in bivariate settings by developing Causal Mosaic, an ensemble framework based on nonlinear ICA that models causal pairs with mixtures of nonlinear models, achieving state-of-the-art performance on artificial and real-world benchmarks.
We address the problem of distinguishing cause from effect in bivariate setting. Based on recent developments in nonlinear independent component analysis (ICA), we train nonparametrically general nonlinear causal models that allow non-additive noise. Further, we build an ensemble framework, namely Causal Mosaic, which models a causal pair by a mixture of nonlinear models. We compare this method with other recent methods on artificial and real world benchmark datasets, and our method shows state-of-the-art performance.