{"total":32,"rows":[{"id":"2307.06250","title":"Identifiability Guarantees for Causal Disentanglement from Soft Interventions","created":"2023-07-12","cited_by_count":84,"area":null},{"id":"2402.05052","title":"Causal Representation Learning from Multiple Distributions: A General Setting","created":"2024-02-07","cited_by_count":68,"area":null},{"id":"2307.02598","title":"Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation","created":"2023-07-05","cited_by_count":58,"area":null},{"id":"2310.15450","title":"General Identifiability and Achievability for Causal Representation Learning","created":"2023-10-24","cited_by_count":34,"area":null},{"id":"2306.07916","title":"Identification of Nonlinear Latent Hierarchical Models","created":"2023-06-13","cited_by_count":27,"area":null},{"id":"2310.02854","title":"Multi-Domain Causal Representation Learning via Weak Distributional Invariances","created":"2023-10-04","cited_by_count":23,"area":null},{"id":"2310.04295","title":"Identifying Representations for Intervention Extrapolation","created":"2023-10-06","cited_by_count":21,"area":null},{"id":"2406.05937","title":"Linear Causal Representation Learning from Unknown Multi-node Interventions","created":"2024-06-09","cited_by_count":14,"area":null},{"id":"2411.06518","title":"Causal Representation Learning from Multimodal Biomedical Observations","created":"2024-11-10","cited_by_count":13,"area":null},{"id":"2504.11609","title":"Towards Interpretable Deep Generative Models via Causal Representation Learning","created":"2025-04-15","cited_by_count":13,"area":null},{"id":"2311.18048","title":"An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis","created":"2023-11-29","cited_by_count":9,"area":null},{"id":"2409.07594","title":"Automated Discovery of Pairwise Interactions from Unstructured Data","created":"2024-09-11","cited_by_count":8,"area":null},{"id":"2410.23620","title":"Identifiability Guarantees for Causal Disentanglement from Purely Observational Data","created":"2024-10-31","cited_by_count":7,"area":null},{"id":"2604.23800","title":"Causal Representation Learning from General Environments under Nonparametric Mixing","created":"2026-04-26","cited_by_count":5,"area":null},{"id":"2401.15903","title":"Toward the Identifiability of Comparative Deep Generative Models","created":"2024-01-29","cited_by_count":4,"area":null},{"id":"2410.20089","title":"Sample Efficient Bayesian Learning of Causal Graphs from Interventions","created":"2024-10-26","cited_by_count":4,"area":null},{"id":"2410.24059","title":"Identifying General Mechanism Shifts in Linear Causal Representations","created":"2024-10-31","cited_by_count":4,"area":null},{"id":"2601.10531","title":"Coarsening Causal DAG Models","created":"2026-01-15","cited_by_count":4,"area":null},{"id":"2603.04780","title":"Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning","created":"2026-03-05","cited_by_count":4,"area":null},{"id":"2403.10978","title":"Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases","created":"2024-03-16","cited_by_count":3,"area":null},{"id":"2509.22196","title":"Mechanistic Independence: A Principle for Identifiable Disentangled Representations","created":"2025-09-26","cited_by_count":3,"area":null},{"id":"2505.01037","title":"Characterization and Learning of Causal Graphs from Hard Interventions","created":"2025-05-02","cited_by_count":2,"area":null},{"id":"2602.05033","title":"Causal Representation Meets Stochastic Modeling under Generic Geometry","created":"2026-02-04","cited_by_count":2,"area":null},{"id":"2604.23790","title":"A General Representation-Based Approach to Multi-Source Domain Adaptation","created":"2026-04-26","cited_by_count":2,"area":null},{"id":"2311.00923","title":"A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representations","created":"2023-11-02","cited_by_count":1,"area":null},{"id":"2407.04605","title":"Linear causal disentanglement via higher-order cumulants","created":"2024-07-05","cited_by_count":1,"area":null},{"id":"2603.25796","title":"Beyond identifiability: Learning causal representations with few environments and finite samples","created":"2026-03-26","cited_by_count":1,"area":null},{"id":"2503.03634","title":"Feature Matching Intervention: Leveraging Observational Data for Causal Representation Learning","created":"2025-03-05","cited_by_count":0,"area":null},{"id":"2507.05412","title":"Incorporating Interventional Independence Improves Robustness against Interventional Distribution Shift","created":"2025-07-07","cited_by_count":0,"area":null},{"id":"2604.17568","title":"Diverse Dictionary Learning","created":"2026-04-19","cited_by_count":0,"area":null},{"id":"2605.12733","title":"From Generalist to Specialist Representation","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2609.32926","title":"Identification of Nonlinear and Dependent Latent Factor Structure through Clique Search","created":"2026-09-26","cited_by_count":0,"area":null}],"next_cursor":null}