Causal Representation Learning on High-Dimensional Data: Benchmarks, Reproducibility, and Evaluation Metrics

ArXi:2603.17405v1 Announce Type: new Causal representation learning (CRL) models aim to transform high-dimensional data into a latent space, enabling interventions to generate counterfactual samples or modify existing data based on the causal relationships among latent variables. To facilitate the development and evaluation of these models, a variety of synthetic and real-world datasets have been proposed, each with distinct advantages and limitations.