{"total":76,"rows":[{"id":"2209.11924","title":"Interventional Causal Representation Learning","created":"2022-09-24","cited_by_count":142,"area":null},{"id":"2306.00542","title":"Nonparametric Identifiability of Causal Representations from Unknown Interventions","created":"2023-06-01","cited_by_count":106,"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":"2305.17225","title":"Causal Component Analysis","created":"2023-05-26","cited_by_count":56,"area":null},{"id":"2111.14820","title":"Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective","created":"2021-11-29","cited_by_count":40,"area":null},{"id":"2207.01603","title":"Invariant and Transportable Representations for Anti-Causal Domain Shifts","created":"2022-07-04","cited_by_count":37,"area":null},{"id":"2210.07347","title":"Disentanglement of Correlated Factors via Hausdorff Factorized Support","created":"2022-10-13","cited_by_count":37,"area":null},{"id":"2206.00801","title":"Indeterminacy in Generative Models: Characterization and Strong Identifiability","created":"2022-06-02","cited_by_count":33,"area":null},{"id":"2402.06665","title":"The Essential Role of Causality in Foundation World Models for Embodied AI","created":"2024-02-06","cited_by_count":32,"area":"wm"},{"id":"2403.00025","title":"On the Challenges and Opportunities in Generative AI","created":"2024-02-28","cited_by_count":31,"area":null},{"id":"2210.14011","title":"Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens","created":"2022-10-25","cited_by_count":28,"area":null},{"id":"2307.01452","title":"Causal Reinforcement Learning: A Survey","created":"2023-07-04","cited_by_count":23,"area":null},{"id":"2304.01391","title":"Counterfactual Learning on Graphs: A Survey","created":"2023-04-03","cited_by_count":22,"area":null},{"id":"2309.12559","title":"Invariant Learning via Probability of Sufficient and Necessary Causes","created":"2023-09-22","cited_by_count":22,"area":null},{"id":"2310.11011","title":"From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling","created":"2023-10-17","cited_by_count":22,"area":null},{"id":"2306.14115","title":"Towards Trustworthy Explanation: On Causal Rationalization","created":"2023-06-25","cited_by_count":19,"area":null},{"id":"2210.10362","title":"CPL: Counterfactual Prompt Learning for Vision and Language Models","created":"2022-10-19","cited_by_count":18,"area":null},{"id":"2310.19691","title":"Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness","created":"2023-10-30","cited_by_count":17,"area":null},{"id":"2311.08815","title":"Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations","created":"2023-11-15","cited_by_count":17,"area":null},{"id":"2410.00903","title":"Causal Inference with Generative Artificial Intelligence: Application to Texts as Treatments","created":"2024-10-01","cited_by_count":16,"area":null},{"id":"2204.04606","title":"Towards efficient representation identification in supervised learning","created":"2022-04-10","cited_by_count":14,"area":null},{"id":"2301.12389","title":"On Learning Necessary and Sufficient Causal Graphs","created":"2023-01-29","cited_by_count":14,"area":null},{"id":"2304.07638","title":"Causal models in string diagrams","created":"2023-04-15","cited_by_count":14,"area":"lr"},{"id":"2504.11609","title":"Towards Interpretable Deep Generative Models via Causal Representation Learning","created":"2025-04-15","cited_by_count":13,"area":null},{"id":"2403.09300","title":"Recursive Causal Discovery","created":"2024-03-14","cited_by_count":12,"area":null},{"id":"2406.13371","title":"Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment","created":"2024-06-19","cited_by_count":12,"area":null},{"id":"2206.13973","title":"Towards a Grounded Theory of Causation for Embodied AI","created":"2022-06-28","cited_by_count":9,"area":null},{"id":"2505.14536","title":"Breaking Bad Tokens: Detoxification of LLMs Using Sparse Autoencoders","created":"2025-05-20","cited_by_count":9,"area":"mechinterp"},{"id":"2207.09370","title":"Data-Centric Epidemic Forecasting: A Survey","created":"2022-07-19","cited_by_count":8,"area":null},{"id":"2305.17139","title":"A Measure-Theoretic Axiomatisation of Causality","created":"2023-05-19","cited_by_count":8,"area":null},{"id":"2310.17325","title":"C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder","created":"2023-10-26","cited_by_count":7,"area":null},{"id":"2409.18581","title":"Deep Autoregressive Models as Causal Inference Engines","created":"2024-09-27","cited_by_count":6,"area":null},{"id":"2503.04556","title":"Compositional Causal Reasoning Evaluation in Language Models","created":"2025-03-06","cited_by_count":6,"area":"lr"},{"id":"2510.23751","title":"Debiasing Reward Models by Representation Learning with Guarantees","created":"2025-10-27","cited_by_count":6,"area":"alignment"},{"id":"2210.13583","title":"Learning Latent Structural Causal Models","created":"2022-10-24","cited_by_count":5,"area":null},{"id":"2405.04834","title":"FlexEControl: Flexible and Efficient Multimodal Control for Text-to-Image Generation","created":"2024-05-08","cited_by_count":5,"area":"diffusion"},{"id":"2407.14058","title":"Towards the Causal Complete Cause of Multi-Modal Representation Learning","created":"2024-07-19","cited_by_count":5,"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":"2202.04136","title":"Generative multitask learning mitigates target-causing confounding","created":"2022-02-08","cited_by_count":4,"area":null},{"id":"2208.08056","title":"Sampling Through the Lens of Sequential Decision Making","created":"2022-08-17","cited_by_count":4,"area":null},{"id":"2409.05872","title":"CSRec: Rethinking Sequential Recommendation from A Causal Perspective","created":"2024-08-23","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":"2506.10378","title":"Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning","created":"2025-06-12","cited_by_count":4,"area":null},{"id":"2202.05737","title":"Improving Generalization via Uncertainty Driven Perturbations","created":"2022-02-11","cited_by_count":3,"area":null},{"id":"2208.12120","title":"Towards Benchmarking Explainable Artificial Intelligence Methods","created":"2022-08-25","cited_by_count":3,"area":null},{"id":"2209.05104","title":"Bias Challenges in Counterfactual Data Augmentation","created":"2022-09-12","cited_by_count":3,"area":null},{"id":"2212.03836","title":"PaDPaF: Partial Disentanglement with Partially-Federated GANs","created":"2022-12-07","cited_by_count":3,"area":null},{"id":"2306.16334","title":"On the Identifiability of Quantized Factors","created":"2023-06-28","cited_by_count":3,"area":null},{"id":"2310.13892","title":"Specify Robust Causal Representation from Mixed Observations","created":"2023-10-21","cited_by_count":3,"area":null},{"id":"2604.14602","title":"CausalDetox: Causal Head Selection and Intervention for Language Model Detoxification","created":"2026-04-16","cited_by_count":3,"area":null},{"id":"2202.06844","title":"On Pitfalls of Identifiability in Unsupervised Learning. A Note on: \"Desiderata for Representation Learning: A Causal Perspective\"","created":"2022-02-14","cited_by_count":2,"area":null},{"id":"2207.07734","title":"COEM: Cross-Modal Embedding for MetaCell Identification","created":"2022-07-15","cited_by_count":2,"area":null},{"id":"2410.08118","title":"Medical Image Quality Assessment based on Probability of Necessity and Sufficiency","created":"2024-10-10","cited_by_count":2,"area":null},{"id":"2411.15605","title":"GIFT: A Framework Towards Global Interpretable Faithful Textual Explanations of Vision Classifiers","created":"2024-11-23","cited_by_count":2,"area":null},{"id":"2507.03897","title":"Leveraging Generative Artificial Intelligence for Causal Inference with Unstructured Data","created":"2025-07-05","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":"2202.13363","title":"Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation","created":"2022-02-27","cited_by_count":1,"area":null},{"id":"2308.09248","title":"Active and Passive Causal Inference Learning","created":"2023-08-18","cited_by_count":1,"area":null},{"id":"2309.13377","title":"Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head","created":"2023-09-23","cited_by_count":1,"area":null},{"id":"2402.06220","title":"A Unified Causal View of Instruction Tuning","created":"2024-02-09","cited_by_count":1,"area":null},{"id":"2403.00352","title":"Revisiting Disentanglement in Downstream Tasks: A Study on Its Necessity for Abstract Visual Reasoning","created":"2024-03-01","cited_by_count":1,"area":"lr"},{"id":"2404.00207","title":"Causal Inference for Human-Language Model Collaboration","created":"2024-03-30","cited_by_count":1,"area":null},{"id":"2510.03727","title":"Bridging the Gap Between Multimodal Foundation Models and World Models","created":"2025-10-04","cited_by_count":1,"area":"wm"},{"id":"2511.03095","title":"Sparse, self-organizing ensembles of local kernels detect rare statistical anomalies","created":"2025-11-05","cited_by_count":1,"area":null},{"id":"2609.38998","title":"Not all solutions are created equal: An analytical dissociation of functional and representational similarity in deep linear neural networks","created":"2026-09-30","cited_by_count":1,"area":null},{"id":"2202.08388","title":"Generalizable Information Theoretic Causal Representation","created":"2022-02-17","cited_by_count":0,"area":null},{"id":"2205.11234","title":"DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation","created":"2022-05-06","cited_by_count":0,"area":null},{"id":"2404.19567","title":"Causal Perception Inspired Representation Learning for Trustworthy Image Quality Assessment","created":"2024-04-30","cited_by_count":0,"area":null},{"id":"2407.00744","title":"Disentangled Representations for Causal Cognition","created":"2024-06-30","cited_by_count":0,"area":"deeprl"},{"id":"2407.16239","title":"Identifiable Latent Bandits: Leveraging observational data for personalized decision-making","created":"2024-07-23","cited_by_count":0,"area":null},{"id":"2408.16577","title":"Seeking the Sufficiency and Necessity Causal Features in Multimodal Representation Learning","created":"2024-08-29","cited_by_count":0,"area":null},{"id":"2502.17538","title":"Policy Learning with a Natural Language Action Space: A Causal Approach","created":"2025-02-24","cited_by_count":0,"area":"alignment"},{"id":"2508.04492","title":"Learning Robust Intervention Representations with Delta Embeddings","created":"2025-08-06","cited_by_count":0,"area":null},{"id":"2603.00289","title":"Seeking Necessary and Sufficient Information from Multimodal Medical Data","created":"2026-02-27","cited_by_count":0,"area":null},{"id":"2604.11663","title":"Why Do Large Language Models Generate Harmful Content?","created":"2026-04-13","cited_by_count":0,"area":"safety"},{"id":"2607.17264","title":"Coordinated Disentanglement with Iterative Mode Discovery Under Hidden Correlations","created":"2026-07-19","cited_by_count":0,"area":null}],"next_cursor":null}